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Introduction

Superframes is a scheme for frame-semantic annotation of text. Every predicate is assigned a frame, and every argument is assigned a role. Superframes annotations can help search corpora for phenomena of interest, enable quantitative cross-lingual comparisons, and support semantic parsing. Superframes aims to be easy to annotate. In particular,

  1. Superframes does not require a lexicon. Frames are coarse and small enough in number to learn them by heart.
  2. Superframes is language-independent.
  3. Superframes ia annotated atop Universal Dependencies, thus delegating many difficult syntax-related decisions to an established framework.

Superframes defines a taxonomy of frames, each of which denotes a relation between two entities and thus defines two roles. Annotating a text comes down to assigning frame labels to content words and role labels to UD dependency edges between them.

Annotating Static Verbs

Consider the following sentence (relevant UD edges shown):

Dependency graph for sentence: Kim owns a house.

Here, we say that the (verbal) predicate owns evokes the POSSESSION frame, which defines the possessum and possessor roles. The possessum roles is filled by a house and the possessor roles is filled by Kim. So we annotate as follows:

Dependency graph for sentence: Kim owns a house. Token owns is labeled POSSESSION and has an edge labeled possessor to token Kim and an edge labeled possessum to token house.

We call the arguments that fill the predicate’s frame’s roles the core arguments. We will turn to non-core arguments later.

Note that semantic roles abstract away from syntactic alternation. As an example, consider the following passive sentences and its Superframes annotation:

Dependency graph for sentence: The house is owned by Kim. Dependency graph for sentence: The house is owned by Kim. Token owned is labeled POSSESSION and has an edge labeled possessum to token house and an edge labeled possessor to token Kim.

Note also that there are only a few dozen superframes, so the meaning they specify is necessarily coarse. Different predicates with similar meaning can invoke the same superframe. This is a feature, as it makes explicit similar predicates and roles.

Dependency graph for sentence: The house belongs to Kim. Token belongs is labeled POSSESSION and has an edge labeled possessum to token house and an edge labeled possessor to token Kim.

Verbs with One Argument

There are, of course, also predicates with only one argument, in which case only one of the two roles is used (most often the first one).

Dependency graph for sentence: Kim is sleeping. Token sleeping is labeled INTERNAL-STATE and has an edge labeled has-state to token Kim.

INTERNAL-STATE is one of those superframes whose second role state is rarely filled by an argument. It denotes an abstract entity (here: the state of being asleep) that is already expressed by the predicate.

Verbs with Zero Arguments

It also happens that predicates occur with no core arguments. One example is weather verbs such as rain. For consistency, there is a rule that says predicates with no core arguments must always be annotated with the most general superframe ENTITY. So that is what happens for example for weather verbs such as rain (which has a syntactic, but semantically empty argument):

Dependency graph for sentence: It is raining. Token raining is labeled ENTITY and has an edge labeled NONCOMP to token It.

We will turn to what NONCOMP means later.

Annotating Dynamic Verbs

Many predicates, especially verbs, do not simply denote a state. Rather, they denote a process working towards the coming about of a state, or a process working against the coming about of a state, or the continuation of a state, or a process working towards the end of a state, or a habitual repeated coming about of a state, or the transitioning through a state, or even a process that transitions through a multitude of states.

Using Aspectual Frames

Superframes inidicates this kind of dynamism through a number of special frames that we call aspectual frames: INIT, PREVENTION, CONTINUATION, DEINIT, HABIT, TRANSIT, DYN. They are combined with ordinary frames through a mechanism called frame stacking. For example:

Dependency graph for sentence: Kim got the house. Token got is labeled POSSESSION-INIT and has an edge labeled possessor to token Kim and an edge labeled possessum to token house.

Here, the predicate got has been annotated with the POSSESSION frame, with Kim as possessor and the house as possessum, as we have seen before. What is new is that the INIT frame has been stacked onto the POSSESSION frame, indicating that the verb denotes not the state of possession, but a process working towards it.

Note

On a technical level, frame stacking indicates that the first frame – here: POSSESSION – fills the first role of the second frame – here: INIT.

Further examples of INIT include:

Dependency graph for sentence: Kim walked towards Boston. Token walked is labeled LOCATION-INIT and has an edge labeled has-location to token Kim and an edge labeled location to token Boston. Dependency graph for sentence: The vase fell to the ground. Token fell is labeled LOCATION-INIT and has an edge labeled has-location to token vase and an edge labeled location to token ground. Dependency graph for sentence: The vase broke. Token broke is labeled INTERNAL-STATE-INIT and has an edge labeled has-state to token vase. Dependency graph for sentence: Kim befriended Sandy. Token befriended is labeled SOCIAL-RELATION-INIT and has an edge labeled socially-related to token Kim and an edge labeled socially-related-to to token Sandy. Dependency graph for sentence: Kim married Sandy. Token married is labeled SOCIAL-RELATION-INIT and has an edge labeled socially-related to token Kim and an edge labeled socially-related-to to token Sandy.

The DEINIT frame indicates a process working towards the end of a state:

Dependency graph for sentence: Kim lost the house. Token lost is labeled POSSESSION-DEINIT and has an edge labeled possessor to token Kim and an edge labeled possessum to token house. Dependency graph for sentence: Kim left Boston. Token left is labeled LOCATION-DEINIT and has an edge labeled has-location to token Kim and an edge labeled location to token Boston. Dependency graph for sentence: Kim divorced Sandy. Token divorced is labeled SOCIAL-RELATION-DEINIT and has an edge labeled socially-related to token Kim and an edge labeled socially-related-to to token Sandy.

The TRANSIT frame indicates a process that transitions through a state where the state does not necessarily exist at the beginning or the end:

Dependency graph for sentence: Kim travelled through Pittsburg. Token travelled is labeled LOCATION-TRANSIT and has an edge labeled has-location to token Kim and an edge labeled location to token Pittsburg.

The CONTINUATION frame indicates that a state persists:

Dependency graph for sentence: Kim kept the house. Token kept is labeled POSSESSION-CONTINUATION and has an edge labeled possessor to token Kim and an edge labeled possessum to token house. Dependency graph for sentence: Kim stayed in Boston. Token stayed is labeled LOCATION-CONTINUATION and has an edge labeled has-location to token Kim and an edge labeled location to token Boston.

The PREVENTION frame indicates that a state fails to come about:

Dependency graph for sentence: Kim was saved from the dragon. Token saved is labeled INFLUENCE-PREVENTION and has an edge labeled influenced to token Kim and an edge labeled influence to token dragon.

The HABIT frame indicates that a state comes about repeatedly, or exists normally:

Dependency graph for sentence: Kim lives in Boston. Token lives is labeled LOCATION-HABIT and has an edge labeled has-location to token Kim and an edge labeled location to token Boston.

Finally, the DYN frame indicates a process that is not straightforwardly described in terms of one or more specific states but instead is a complex process transitioning through a multitude of states. Because these states can be very heterogeneous, we usually stack DYN on top of the very general SITUATION frame.

Dependency graph for sentence: Kim is partying. Token partying is labeled SITUATION-DYN and has an edge labeled situee to token Kim.

Stacking Aspectual Frames Onto Roles

Some predicates take multiple arguments wrt. different phases of the process. For these cases, there is a special notation where you can stack an aspectual frame atop a role. For example:

Dependency graph for sentence: Kim traveled from Chicago via Pittsburgh to Boston. Token traveled is labeled LOCATION-INIT and has an edge labeled has-location to token Kim and an edge labeled location-DEINIT to token Chicago and an edge labeled location-TRANSIT to token Pittsburgh and an edge labeled location to token Boston. Dependency graph for sentence: Kim sold Sandy the house. Token sold is labeled POSSESSION-DEINIT and has an edge labeled possessor to token Kim and an edge labeled possessor-INIT to token Sandy and an edge labeled possessum to token house. Dependency graph for sentence: Sandy bought the house from Kim. Token bought is labeled POSSESSION-INIT and has an edge labeled possessor to token Sandy and an edge labeled possessum to token house and an edge labeled possessor-DEINIT to token Kim. Dependency graph for sentence: Sandy owes Sandy money. Token owes is labeled POSSESSION-DEINIT-NECESSITY and has an edge labeled possessor to token Sandy and an edge labeled possessor-INIT to token Sandy and an edge labeled possessum to token money.

Using Aspectual Frames by Themselves

Some verbs take another predicate (more precisely: a phrase headed by another predicate) as an argument and contribute an aspectual meaning. We call such predicates matrix predicates, taking an embedded predicate. In such cases, use aspectual frames by themselves, without stacking.

Dependency graph for sentence: The concert began. Token began is labeled INIT and has an edge labeled initiated to token concert. Dependency graph for sentence: The concert continued. Token continued is labeled CONTINUATION and has an edge labeled continued to token concert. Dependency graph for sentence: The concert finished. Token finished is labeled DEINIT and has an edge labeled deinitiated to token concert. Dependency graph for sentence: The shouting faded. Token faded is labeled DEINIT and has an edge labeled deinitiated to token shouting. Dependency graph for sentence: A coup was attempted. Token attempted is labeled INIT and has an edge labeled initiated to token coup. Dependency graph for sentence: An outbreak was prevented. Token prevented is labeled PREVENTION and has an edge labeled prevented to token outbreak.

Using Aspectual Frames with the META Frame

Some matrix verbs take over the syntactic subject of their embedded predicate in what is known as a control construction. In such cases, we stack an aspectual frame over the special META frame. Its scene role is filled by the embedded predicate, and its participant role is filled by the controlled subject. It is then required to add a semantic dependency from the embedded predicate to the controlled subject, even though no such syntactic dependency exists in (basic) UD.

Dependency graph for sentence: Kim refrained from going. Token refrained is labeled META-PREVENTION and has an edge labeled participant to token Kim and an edge labeled scene to token going. Token going is labeled LOCATION-INIT and has an edge labeled has-location to token Kim. Dependency graph for sentence: Kim finished their work. Token finished is labeled META-DEINIT and has an edge labeled participant to token Kim and an edge labeled scene to token work. Token work is labeled SITUATION-DYN and has an edge labeled situee to token Kim.

Annotating Non-core Arguments

So far, we have only seen predicates with at most two arguments, all of whose roles are defined by the the superframe that the predicate is annotated with (the core frame). But many verbs have more than two arguments, or otherwise arguments with roles from different frames. One very common example is causative subjects, denoting an entity presented as causing the situation denoted by the core frame. For example, consider the following:

Dependency graph for sentence: The vase broke. Token broke is labeled INTERNAL-STATE-INIT and has an edge labeled has-state to token vase. Dependency graph for sentence: Kim broke the vase. Token broke is labeled INTERNAL-STATE-INIT and has an edge labeled CAUSER to token Kim and an edge labeled has-state to token vase.

In both sentences, the core frame is INTERNAL-STATE-INIT, with the vase as the has-state argument. In the second sentence, Kim is added as another argument, presented as the causer of the situation denoted by the core frame (the breaking of the vase). We thus annotate it with the causer role from the CAUSATION frame. To distinguish them from core arguments, non-core arguments are written in all upper case.

Note

On a technical level, the non-core CAUSATION frame is interpreted as being added “around” the core INTERNAL-STATE-INIT frame so that the latter fills the former other role (the effect role). This makes sense: the vase breaking is the effect here.

Similar examples include:

Dependency graph for sentence: Kim threw the vase to the ground. Token threw is labeled LOCATION-INIT and has an edge labeled CAUSER to token Kim and an edge labeled has-location to token vase and an edge labeled location to token ground. Dependency graph for sentence: Kim gave Sandy a book. Token gave is labeled POSSESSION-INIT and has an edge labeled CAUSER to token Kim and an edge labeled possessor to token Sandy and an edge labeled possessum to token book. Dependency graph for sentence: Alex introduced Kim to Sandy. Token introduced is labeled SOCIAL-RELATION-INIT and has an edge labeled CAUSER to token Alex and an edge labeled socially-related to token Kim and an edge labeled socially-related-to to token Sandy. Dependency graph for sentence: The official married Kim to Sandy. Token married is labeled SOCIAL-RELATION-INIT and has an edge labeled CAUSER to token official and an edge labeled socially-related to token Kim and an edge labeled socially-related-to to token Sandy. Dependency graph for sentence: Kim prevented Sandy from going. Token prevented is labeled META-PREVENTION and has an edge labeled CAUSER to token Kim and an edge labeled participant to token Sandy and an edge labeled scene to token going. Token going is labeled LOCATION-INIT and has an edge labeled has-location to token Sandy. Dependency graph for sentence: Swift action prevented an outbreak. Token prevented is labeled PREVENTION and has an edge labeled CAUSER to token action and an edge labeled prevented to token outbreak.

Two other very common non-core arguments are the sender and recipients (experiencers) of messages.

Dependency graph for sentence: Kim talked to Sandy about Bali. Token talked is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled EXPERIENCER to token Sandy and an edge labeled topic to token Bali.

Other non-core arguments are usually rather predicate-specific.

Dependency graph for sentence: Kim searched the woods for Sandy. Token searched is labeled MESSAGE-INIT and has an edge labeled EXPERIENCER to token Kim and an edge labeled LOCATION to token woods and an edge labeled topic to token Sandy. Dependency graph for sentence: Kim bought the house for a million dollars. Token bought is labeled POSSESSION-INIT and has an edge labeled possessor to token Kim and an edge labeled possessum to token house and an edge labeled ASSET to token dollars.

Annotating Nouns and Pronouns

So far, we have only looked at verbal predicates. But nouns can have (core) arguments too. Specifically, this is true for event nouns and relational nouns.

Event nouns are annotated much like verbs:

Dependency graph for sentence: Kim ’s breaking of the vase. Token breaking is labeled INTERNAL-STATE-INIT and has an edge labeled CAUSER to token Kim and an edge labeled has-state to token vase. Dependency graph for sentence: Kim ’s height. Token height is labeled PROPERTY and has an edge labeled has-property to token Kim. Dependency graph for sentence: Sandy ’s destruction. Token destruction is labeled EXISTENCE-DEINIT and has an edge labeled existent to token Sandy. Dependency graph for sentence: Sandy ’s destruction of the city. Token destruction is labeled EXISTENCE-DEINIT and has an edge labeled CAUSER to token Sandy and an edge labeled existent to token city.

Relational nouns are not much different, except that they additionally refer to one of the participants in the relation. But we are only concerned with evocation, not with reference.

Dependency graph for sentence: Kim ’s friend. Token friend is labeled SOCIAL-RELATION and has an edge labeled socially-related to token Kim. Dependency graph for sentence: my critic. Token critic is labeled EVALUATION and has an edge labeled evaluee to token my. Dependency graph for sentence: He is our physics teacher. Token teacher is labeled MESSAGE-HABIT and has an edge labeled SENDER to token He and an edge labeled EXPERIENCER to token our and an edge labeled topic to token physics. Dependency graph for sentence: She is the president of our club. Token president is labeled OBLIGATION and has an edge labeled obliged to token She and an edge labeled obliged-to to token club.

Many nouns, of course, are neither event nouns nor relational nouns, and thus do not have any (core) arguments. The includes proper nouns and is also true for most pronouns. Thus, the rule that we mentioned earlier applies: an occurrence of a predicate without any core arguments is always annotated with the special superframe ENTITY.

Dependency graph for sentence: a tree. Token tree is labeled ENTITY. Dependency graph for sentence: Kim. Token Kim is labeled ENTITY. Dependency graph for sentence: they. Token they is labeled ENTITY.

Occasionally, pronouns do have arguments. In that case, frame them like you would the antecedent noun:

Dependency graph for sentence: The picture was that of the boa. Token that is labeled MESSAGE and has an edge labeled SAME to token picture and an edge labeled topic to token boa.

Annotating Modifiers

Nominal modifiers of nouns (UD relation nmod), oblique dependents of verbs (obl) that are not annotated as core arguments, as well as adverbial clauses (advcl) are annotated much like non-core arguments – with a capitalized role label that is short for the corresponding frame, with the modified element implictly filling the other role of that frame. For example:

Dependency graph for sentence: Kim is sweating in the sauna. Token sweating is labeled CONTAINMENT-DEINIT and has an edge labeled container to token Kim and an edge labeled LOCATION to token sauna. Dependency graph for sentence: Wir machen das wegen der Kinder. Token machen is labeled CAUSATION and has an edge labeled causer to token Wir and an edge labeled effect to token das and an edge labeled REASON to token Kinder. Dependency graph for sentence: gekommen , um zu bleiben. Token gekommen is labeled LOCATION-INIT and has an edge labeled END to token bleiben. Dependency graph for sentence: a tree in the garden. Token tree is labeled ENTITY and has an edge labeled LOCATION to token garden. Dependency graph for sentence: Kim ’s tree. Token tree is labeled ENTITY and has an edge labeled POSSESSOR to token Kim.

Annotating Adjectives, Adverbs, and Relative Clauses

Predicative adjectives are annotated much like verbs:

Dependency graph for sentence: I am despicable. Token despicable is labeled PROPERTY and has an edge labeled has-property to token I. Dependency graph for sentence: The dog is tired. Token tired is labeled INTERNAL-STATE and has an edge labeled has-state to token dog. Dependency graph for sentence: Change is necessary. Token necessary is labeled NECESSITY and has an edge labeled necessary to token Change. Dependency graph for sentence: Change is possible. Token possible is labeled POSSIBILITY and has an edge labeled possible to token Change.

Attributive adjectives and verbs behave syntactically like modifiers, but they are also predicates assigning the modifiee a role. For the modification relation, we use the META frame, assigning the modifying predicate the scene role and – implicitly – the modifiee the participant role. We then introduce an additional semantic dependency (we call it a backlink) from the modifying predicate to the modifiee to annotate the role that the former assigns to the latter. For example:

Dependency graph for sentence: despicable me. Token despicable is labeled PROPERTY and has an edge labeled has-property to token me. Token me is labeled ENTITY and has an edge labeled SCENE to token despicable. Dependency graph for sentence: the tired dog. Token tired is labeled INTERNAL-STATE and has an edge labeled has-state to token dog. Token dog is labeled ENTITY and has an edge labeled SCENE to token tired. Dependency graph for sentence: Kim ran fast. Token ran is labeled LOCATION-DYN and has an edge labeled has-location to token Kim and an edge labeled SCENE to token fast. Token fast is labeled PROPERTY and has an edge labeled has-property to token ran. Dependency graph for sentence: Kim ran far. Token ran is labeled LOCATION-DYN and has an edge labeled has-location to token Kim and an edge labeled SCENE to token far. Token far is labeled QUANTITY and has an edge labeled has-quantity to token ran. Dependency graph for sentence: Ich spiele lieber Schach. Token spiele is labeled META and has an edge labeled participant to token Ich and an edge labeled SCENE to token lieber and an edge labeled scene to token Schach. Token lieber is labeled EVALUATION and has an edge labeled evaluee to token spiele. Token Schach is labeled SITUATION-DYN and has an edge labeled situee to token Ich. Dependency graph for sentence: Der ist sowieso kaputt. Token sowieso is labeled CONDITION and has an edge labeled has-condition to token kaputt. Token kaputt is labeled INTERNAL-STATE and has an edge labeled has-state to token Der and an edge labeled SCENE to token sowieso. Dependency graph for sentence: Und doch sah er intelligent aus. Token doch is labeled CONCESSION and has an edge labeled asserted to token sah. Token sah is labeled MESSAGE and has an edge labeled SCENE to token doch and an edge labeled topic to token er and an edge labeled message to token intelligent and an edge labeled NONCOMP to token aus. Dependency graph for sentence: Sie sprangen des Regens ungeachtet nach draußen. Token sprangen is labeled LOCATION-INIT and has an edge labeled has-location to token Sie and an edge labeled SCENE to token ungeachtet and an edge labeled LOCATION to token draußen. Token ungeachtet is labeled CONCESSION and has an edge labeled conceded to token Regens. Dependency graph for sentence: Kim war unvermindert fröhlich. Token unvermindert is labeled QUANTITY-CONTINUATION and has an edge labeled has-quantity to token fröhlich. Token fröhlich is labeled INTERNAL-STATE and has an edge labeled has-state to token Kim and an edge labeled SCENE to token unvermindert. Dependency graph for sentence: » Wir haben um Hilfe gebeten « , so Saqib. Token gebeten is labeled NORM and has an edge labeled SENDER to token Wir and an edge labeled norm to token Hilfe and an edge labeled SCENE to token so. Token so is labeled SENDING and has an edge labeled sent to token gebeten and an edge labeled sender to token Saqib. Dependency graph for sentence: ein anderes Plakat. Token anderes is labeled COMPARISON and has an edge labeled compared to token Plakat. Token Plakat is labeled ENTITY and has an edge labeled SCENE to token anderes.

Relative clauses work very similarly: they modify a noun but also act as predicates assigning that noun a role. Again, we use SCENE for the acl:relcl relation, and a backlink to the modified noun. This is in addition to the dependency to any relative pronoun, which has the same role.

Dependency graph for sentence: the song that I like. Token song is labeled ENTITY and has an edge labeled EXPERIENCER to token I and an edge labeled SCENE to token like. Token like is labeled EVALUATION and has an edge labeled evaluee to token song and an edge labeled evaluee to token that. Dependency graph for sentence: the song I like. Token song is labeled ENTITY and has an edge labeled EXPERIENCER to token I and an edge labeled SCENE to token like. Token like is labeled EVALUATION and has an edge labeled evaluee to token song.

Annotating Nonlocal Dependencies

Nonlocal dependencies are dependencies from one clause into another. They do not exist as syntactic dependencies in (basic) UD, so for Superframes annotation, we add them when needed. We have already seen nonlocal dependencies in the previous chapter with attributive adjectives, adverbs, and relative clauses: these elements are attached to their head using the META frame (manifesting as a SCENE modifying relation), and then a nonlocal dependency (a “backlink”) is added from the modifying element back to its head.

There are a number of other constructions that systematically introduce nonlocal dependencies, which we also annotate. We give examples of each in the following.

Subject control:

Dependency graph for sentence: Kim promised Sandy to come. Token promised is labeled NORM-INIT and has an edge labeled SENDER to token Kim and an edge labeled EXPERIENCER to token Sandy and an edge labeled norm to token come. Token come is labeled LOCATION-INIT and has an edge labeled has-location to token Kim. Dependency graph for sentence: Kim used a hammer to smash the vase. Token used is labeled MEANS and has an edge labeled CAUSER to token Kim and an edge labeled means to token hammer and an edge labeled end to token smash. Token smash is labeled INTERNAL-STATE-INIT and has an edge labeled CAUSER to token Kim and an edge labeled has-state to token vase.

Object control:

Dependency graph for sentence: Kim persuaded Sandy to come. Token persuaded is labeled NORM-INIT and has an edge labeled SENDER to token Kim and an edge labeled EXPERIENCER to token Sandy and an edge labeled norm to token come. Token come is labeled LOCATION-INIT and has an edge labeled has-location to token Sandy.

Non-obligatory control:

Dependency graph for sentence: Kim left after trashing the room. Token left is labeled LOCATION-DEINIT and has an edge labeled has-location to token Kim and an edge labeled PRECEDENT to token trashing. Token trashing is labeled INTERNAL-STATE-INIT and has an edge labeled CAUSER to token Kim and an edge labeled has-state to token room. Dependency graph for sentence: Kim has come to stay. Token come is labeled LOCATION-INIT and has an edge labeled has-location to token Kim and an edge labeled END to token stay. Token stay is labeled LOCATION-CONTINUATION and has an edge labeled has-location to token Kim.

Non-obligatory control with parasitic gap:

Dependency graph for sentence: the question we raised without answering. Token question is labeled ENTITY and has an edge labeled SCENE to token raised. Token raised is labeled MESSAGE-INIT and has an edge labeled message to token question and an edge labeled SENDER to token we and an edge labeled ACCOMPANIER-NEG to token answering. Token answering is labeled MESSAGE-INIT and has an edge labeled topic to token question and an edge labeled SENDER to token we.

Raising:

Dependency graph for sentence: Kim seemed to fly. Token seemed is labeled MESSAGE and has an edge labeled topic to token Kim and an edge labeled message to token fly. Token fly is labeled LOCATION-DYN and has an edge labeled has-location to token Kim.

Secondary predicate (see also the section on secondary predicates below):

Dependency graph for sentence: Kim entered the room singing. Token entered is labeled LOCATION-INIT and has an edge labeled has-location to token Kim and an edge labeled location to token room and an edge labeled ACCOMPANIER to token singing. Token singing is labeled SENDING and has an edge labeled sender to token Kim.

Tough construction:

Dependency graph for sentence: Kim is hard to love. Token hard is labeled META-REALITY and has an edge labeled participant to token Kim and an edge labeled scene to token love. Token love is labeled EVALUATION and has an edge labeled evaluee to token Kim.

Relative clause:

Dependency graph for sentence: the song that I like. Token song is labeled ENTITY and has an edge labeled EXPERIENCER to token I and an edge labeled SCENE to token like. Token like is labeled EVALUATION and has an edge labeled evaluee to token song and an edge labeled evaluee to token that.

Reduced relative clause:

Dependency graph for sentence: the song I like. Token song is labeled ENTITY and has an edge labeled EXPERIENCER to token I and an edge labeled SCENE to token like. Token like is labeled EVALUATION and has an edge labeled evaluee to token song.

Non-finite reduced relative clause:

Dependency graph for sentence: the song liked by Kim. Token song is labeled ENTITY and has an edge labeled SCENE to token liked. Token liked is labeled EVALUATION and has an edge labeled evaluee to token song and an edge labeled EXPERIENCER to token Kim. Dependency graph for sentence: students living on campus. Token students is labeled ENTITY and has an edge labeled SCENE to token living. Token living is labeled LOCATION-HABIT and has an edge labeled has-location to token students and an edge labeled location to token campus.

Relative clause with complex extracted element:

Dependency graph for sentence: eine Gestalt , deren Magerkeit durch den Trainingsanzug noch betont wurde. Token Gestalt is labeled ENTITY and has an edge labeled SCENE to token betont. Token Magerkeit is labeled PROPERTY and has an edge labeled has-property to token Gestalt. Token noch is labeled ACCOMPANIMENT and has an edge labeled accompanier to token betont. Token betont is labeled MESSAGE and has an edge labeled topic to token Magerkeit and an edge labeled SENDER to token Trainingsanzug and an edge labeled SCENE to token noch.

Relative clause with extraction across clause boundaries:

Dependency graph for sentence: Atmosphäre , mit der sie sich zu umgeben wusste. Token Atmosphäre is labeled ENTITY and has an edge labeled SCENE to token wusste. Token umgeben is labeled SITUATION-INIT and has an edge labeled situator to token Atmosphäre and an edge labeled situator to token der and an edge labeled situee to token sich. Token wusste is labeled META-POSSIBILITY and has an edge labeled participant to token sie and an edge labeled scene to token umgeben.

Coordination:

Dependency graph for sentence: ein sogenannter Televisor oder Hörsehschirm. Token sogenannter is labeled IDENTIFIER-INIT and has an edge labeled identified to token Televisor and an edge labeled identified to token Hörsehschirm. Token Televisor is labeled ENTITY and has an edge labeled SCENE to token sogenannter. Token Hörsehschirm is labeled ENTITY and has an edge labeled SCENE to token sogenannter.

Secondary Predicates

Secondary predicates are modifiers that syntactically attach to a (primary) predicate, but semantically predicate over one of the primary predicate’s arguments, or even something more deeply embedded. The semantic relation between the primary and the secondary predicate can be one of simple accompaniment (these secondary predicates are also called depictives), effect (these secondary predicates are also called resultatives), or something else.

Dependency graph for sentence: Kim entered the room singing. Token entered is labeled LOCATION-INIT and has an edge labeled has-location to token Kim and an edge labeled location to token room and an edge labeled ACCOMPANIER to token singing. Token singing is labeled SENDING and has an edge labeled sender to token Kim. Dependency graph for sentence: You ’re talking me silly. Token talking is labeled SENDING and has an edge labeled sender to token You and an edge labeled EFFECT-INIT to token me and an edge labeled EFFECT-INIT to token silly. Token silly is labeled INTERNAL-STATE and has an edge labeled has-state to token me. Dependency graph for sentence: Stop drawing sheep , whether small or big. Token Stop is labeled META-DEINIT and has an edge labeled scene to token drawing and an edge labeled CONDITION to token small and an edge labeled CONDITION to token big. Token drawing is labeled MESSAGE-INIT and has an edge labeled topic to token sheep. Token small is labeled PROPERTY and has an edge labeled has-property to token sheep. Token big is labeled PROPERTY and has an edge labeled has-property to token sheep. Dependency graph for sentence: Some people , though short , reach amazing heights. Token short is labeled PROPERTY and has an edge labeled has-property to token people. Token reach is labeled META-INIT and has an edge labeled participant to token people and an edge labeled CONCEDED to token short and an edge labeled scene to token heights. Token amazing is labeled PROPERTY and has an edge labeled has-property to token heights. Token heights is labeled PROPERTY and has an edge labeled has-property to token people.

Note that in the resultative example You’re talking me silly, the primary predicate talking does not assign a role to its syntactic object me, only the secondary predicate silly does. But the annotation scheme still forces us to label the dependency from talking to me. We solve this by labeling it EFFECT-INIT too, just like the dependency to the secondary predicate.

Principles for Annotation

Work in progress

  • MESSAGE vs. META-REALITY (Kim seems happy)
  • prefer core over non-core roles if a dependent fills both (Kim bought a house, Kim wants to swim)
  • frames must be associated with roots: Kim plays tennis (META, not META-HABIT) vs. Kim used to play tennis (META-HABIT-TIME)?
  • terminology: (syntactic) dependents are arguments if assigned a role by the head
  • terminology: situations, scenes, eventualities, states, events

ENTITY

This is the top of the superframe hierarchy, i.e., the most general frame. It is also the only frame that defines 0 rather than 2 roles. It is used for all those and only those occurrences of content words that do not have any core arguments, including all pronouns and all proper names.

Dependency graph for sentence: Chapter 1. Token Chapter is labeled ENTITY and has an edge labeled SCENE to token 1. Token 1 is labeled RANK and has an edge labeled has-rank to token Chapter. Dependency graph for sentence: I. Token I is labeled ENTITY. Dependency graph for sentence: a magnificent picture. Token magnificent is labeled PROPERTY and has an edge labeled has-property to token picture. Token picture is labeled ENTITY and has an edge labeled SCENE to token magnificent. Dependency graph for sentence: an animal. Token animal is labeled ENTITY. Dependency graph for sentence: a drawing. Token drawing is labeled ENTITY. Dependency graph for sentence: Kim. Token Kim is labeled ENTITY.

Note that we go strictly by occurrence: the same word in another context may well have core arguments and thus be framed differently.

Dependency graph for sentence: a drawing of an elephant. Token drawing is labeled MESSAGE and has an edge labeled topic to token elephant.

SITUATION

Roles: situee, situator

This superframe is the ancestor of all other superframes except ENTTIY. It denotes a situation in the most general sense: some entity (the situee) is in a situation, whatever the nature of that situation may be. If there is another core argument, it is labeled situator and the situation is then some sort of relation between two entities. There are four usual ways to use this frame:

Stacked with -DYN

Used for predicates that describe processes that cannot easily be framed in terms of a state but that involve complex transitions through multiple hetereogeneous states:

Dependency graph for sentence: Kim was partying. Token partying is labeled SITUATION-DYN and has an edge labeled situee to token Kim.

Stacked with an Aspectual or Modal Frame

This often occurs with predicates that focus on aspect or made, leaving the precise nature of the relation between the two arguments underspecified.

Dependency graph for sentence: transition of the account to a new government. Token transition is labeled SITUATION-INIT and has an edge labeled situee to token account and an edge labeled situator to token government. Dependency graph for sentence: they need three months. Token need is labeled SITUATION-NECESSITY and has an edge labeled situee to token they and an edge labeled situator to token months.

As a Default Frame

Use SITUATION if an entity is described as being in a state, but it is not internal and there is not other frame that seems to fit better.

Dependency graph for sentence: I was los. Token los is labeled SITUATION and has an edge labeled situee to token I.

ABSTRACT-LOCATION

Roles: has-abstract-location, abstract-location

LOCATION

Roles: has-location, location

CONTACT

Roles: ornament, surface

CONTAINMENT

Roles: contained, container

ORIENTATION

Roles: oriented, landmark

WRAPPING-WEARING

Roles: wrapper, wearer

ABSTRACT-POSSESSION

Roles: abstract-possessum, abstract-possessor

POSSESSION

Roles: possessum, possessor

ACCOMPANIMENT

Roles: accompanied, accompanier

accompanier accompanies accompanied, meaning that it occurs together with it or participates equally in the same event.

Dependency graph for sentence: veggies with rice. Token veggies is labeled ENTITY and has an edge labeled ACCOMPANIER to token rice. Dependency graph for sentence: The veggies come with rice. Token come is labeled ACCOMPANIMENT and has an edge labeled accompanied to token veggies and an edge labeled accompanier to token rice. Dependency graph for sentence: Kim added rice to the veggies. Token added is labeled ACCOMPANIMENT-INIT and has an edge labeled CAUSER to token Kim and an edge labeled accompanier to token rice and an edge labeled accompanied to token veggies. Dependency graph for sentence: Rolling thunder accompanies the rain. Token accompanies is labeled ACCOMPANIMENT and has an edge labeled accompanier to token thunder and an edge labeled accompanier to token rain.

Often, the accompanier denotes not the accompanying event but an entity participating in it, and must be metonymically understood as the event.

Dependency graph for sentence: Kim cycled to Rome with Sandy. Token cycled is labeled LOCATION-INIT and has an edge labeled has-location to token Kim and an edge labeled location to token Rome and an edge labeled ACCOMPANIER to token Sandy. Dependency graph for sentence: Kim danced with Sandy. Token danced is labeled SITUATION-DYN and has an edge labeled situee to token Kim and an edge labeled ACCOMPANIER to token Sandy. Dependency graph for sentence: Kim accompanied Sandy. Token accompanied is labeled ACCOMPANIMENT-DYN and has an edge labeled accompanier to token Kim and an edge labeled accompanied to token Sandy. Dependency graph for sentence: Kim accompanied Sandy on the piano. Token accompanied is labeled ACCOMPANIMENT-DYN and has an edge labeled accompanier to token Kim and an edge labeled accompanied to token Sandy and an edge labeled MEANS to token piano.

COMPARISON

Roles: compared, reference

compared is characterized with respect to reference.

Dependency graph for sentence: Compared with Sandy , Kim is tall. Token Compared is labeled COMPARISON and has an edge labeled reference to token Sandy. Token tall is labeled PROPERTY and has an edge labeled SCENE to token Compared and an edge labeled has-property to token Kim. Dependency graph for sentence: Sandy is short whereas Kim is tall. Token short is labeled PROPERTY and has an edge labeled has-property to token Sandy and an edge labeled REFERENCE to token tall. Token tall is labeled PROPERTY and has an edge labeled has-property to token Kim. Dependency graph for sentence: They demonize the left while doing nothing about the right. Token demonize is labeled EVALUATION and has an edge labeled SENDER to token They and an edge labeled evaluee to token left. Token doing is labeled SITUATION-INIT and has an edge labeled CAUSER to token They and an edge labeled situator to token nothing and an edge labeled situee to token right. Dependency graph for sentence: Kim exceeds Sandy in height. Token exceeds is labeled COMPARISON and has an edge labeled compared to token Kim and an edge labeled reference to token Sandy and an edge labeled PERTAINED-TO to token height. Dependency graph for sentence: The Polish restaurant compared favorably to the Spanish one. Token compared is labeled COMPARISON and has an edge labeled SCENE to token favorably and an edge labeled reference to token one. Token favorably is labeled PROPERTY and has an edge labeled has-property to token compared. Dependency graph for sentence: Kim compared Coke to Pepsi. Token compared is labeled COMPARISON and has an edge labeled EXPERIENCER to token Kim and an edge labeled compared to token Coke and an edge labeled reference to token Pepsi. Dependency graph for sentence: I was more isolated than a shipwrecked sailor. Token more is labeled QUANTITY and has an edge labeled has-quantity to token isolated. Token isolated is labeled LOCATION-NEG and has an edge labeled has-location to token I and an edge labeled SCENE to token more and an edge labeled REFERENCE to token sailor. Dependency graph for sentence: Kim is taller than Sandy. Token taller is labeled PROPERTY and has an edge labeled has-property to token Kim and an edge labeled REFERENCE to token Sandy. Dependency graph for sentence: die unter allen Provinzen am drittstärksten bevölkerte. Token drittstärksten is labeled QUANTITY and has an edge labeled has-quantity to token bevölkerte. Token bevölkerte is labeled ENTITY and has an edge labeled REFERENCE to token Provinzen and an edge labeled SCENE to token drittstärksten. Dependency graph for sentence: Kim outranks Sandy. Token outranks is labeled RANK and has an edge labeled has-rank to token Kim and an edge labeled REFERENCE to token Sandy. Dependency graph for sentence: Kim outshines Sandy. Token outshines is labeled SENDING and has an edge labeled sender to token Kim and an edge labeled REFERENCE to token Sandy. Dependency graph for sentence: Sie versuchten, die Stimme zu übertönen. Token übertönen is labeled SENDING and has an edge labeled sender to token Sie and an edge labeled REFERENCE to token Stimme.

reference need not be an entity similar to the compared, it can also be an abstract constraint:

Dependency graph for sentence: The program conforms to the spec. Token conforms is labeled COMPARISON and has an edge labeled compared to token program and an edge labeled reference to token spec. Dependency graph for sentence: Kim ran afoul of Fielding ’s constraints. Token ran is labeled META-INIT and has an edge labeled participant to token Kim. Token afoul is labeled COMPARISON-NEG and has an edge labeled compared to token Kim and an edge labeled reference to token constraints.

CONCESSION

Roles: asserted, conceded

Special case of COMPARISON, where compared is what’s asserted and reference is what’s conceded.

Dependency graph for sentence: Kim went out despite the rain. Token went is labeled LOCATION-INIT and has an edge labeled has-location to token Kim and an edge labeled location to token out and an edge labeled CONCEDED to token rain. Dependency graph for sentence: It rained , but Kim went out. Token rained is labeled ENTITY and has an edge labeled NONCOMP to token It and an edge labeled ASSERTED to token went. Dependency graph for sentence: Kim sent Sandy a message , but it never arrived. Token sent is labeled SENDING and has an edge labeled sender to token Kim and an edge labeled EXPERIENCER to token Sandy and an edge labeled sent to token message and an edge labeled ASSERTED to token arrived. Dependency graph for sentence: Kim went although Sandy had told them not to. Token went is labeled LOCATION-INIT and has an edge labeled has-location to token Kim and an edge labeled CONCEDED to token told. Dependency graph for sentence: man wurde gegen seinen Willen in einen Verrückten verwandelt. Token Verrückten is labeled PROPERTY and has an edge labeled has-property to token man. Token verwandelt is labeled META-INIT and has an edge labeled participant to token man and an edge labeled CONCEDED to token Willen and an edge labeled scene to token Verrückten.

IDENTITY

Roles: same, same-as

same is the same entity as same-as.

Dependency graph for sentence: Bruce Wayne is Batman. Token Batman is labeled ENTITY and has an edge labeled SAME to token Wayne. Dependency graph for sentence: It was the drawing of the boa constrictor. Token drawing is labeled MESSAGE and has an edge labeled SAME to token It and an edge labeled topic to token boa. Dependency graph for sentence: That is why I stayed. Token why is labeled REASON and has an edge labeled has-reason to token stayed. Token stayed is labeled LOCATION-CONTINUATION and has an edge labeled SAME to token That and an edge labeled SCENE to token why and an edge labeled has-location to token I. Dependency graph for sentence: the Desert of Sahara. Token Desert is labeled ENTITY and has an edge labeled SAME to token Sahara. Dependency graph for sentence: the experiment of showing him my Drawing Number One. Token experiment is labeled ENTITY and has an edge labeled SAME to token showing.

FEATURE

Roles: feature, has-feature

MATERIAL

Roles: material, object

PART-WHOLE

Roles: part, whole

PHASE

Roles: phase, whole-scene

SUBCLASS

Roles: subclass, superclass

SUBSET

Roles: subset, superset

IDENTIFIER

Roles: identified, identifier

INFLUENCE

Roles: influenced, influence

CAUSATION

Roles: effect, causer

CREATION

Roles: creation, creator

MEANS

Roles: end, means

ASSET

Roles: has-asset, asset

MEDIUM

Roles: transmission, medium

SENDING

Roles: sent, sender

sender originates a message, sent, that can be perceived.

Dependency graph for sentence: Kim yelped. Token yelped is labeled SENDING-INIT and has an edge labeled sender to token Kim. Dependency graph for sentence: Kim and Sandy conversed. Token conversed is labeled SENDING-INIT and has an edge labeled sender to token Kim and an edge labeled sender to token Sandy. Dependency graph for sentence: Kim conversed with Sandy. Token conversed is labeled SENDING-INIT and has an edge labeled sender to token Kim and an edge labeled ACCOMPANIER to token Sandy. Dependency graph for sentence: Kim curtseyed to the Queen. Token curtseyed is labeled SENDING-INIT and has an edge labeled sender to token Kim and an edge labeled EXPERIENCER to token Queen. Dependency graph for sentence: Kim shook their head. Token shook is labeled SENDING-INIT and has an edge labeled sender to token Kim and an edge labeled NONCOMP to token head.

PERFORMANCE

Roles: work, performer

performer performs a work (of art).

Dependency graph for sentence: Kim played a little tune on their tuba. Token played is labeled PERFORMANCE-INIT and has an edge labeled performer to token Kim and an edge labeled work to token tune and an edge labeled MEANS to token tuba. Dependency graph for sentence: They performed the play. Token performed is labeled PERFORMANCE-INIT and has an edge labeled performer to token They and an edge labeled work to token play. Dependency graph for sentence: Kim sang a song. Token sang is labeled PERFORMANCE-INIT and has an edge labeled performer to token Kim and an edge labeled work to token song.

CONDITION

Roles: has-condition, condition

EXCEPTION

Roles: has-exception, exception

LIMITATION

Roles: is-limited, limitation

PROPORTION

Roles: numerator, denominator

COPY

Roles: copy, original

EXPERIENCE

Roles: experiencer, experienced

REACTION

Roles: reaction, trigger

REASON

Roles: has-reason, reason

INSTANCE

Roles: instance, class

INTERNAL-STATE

Roles: has-state, state

POSE

Roles: has-pose, pose

PROPERTY

Roles: has-property, property

DYN

Roles: has-dyn, dyn

CONTINUATION

Roles: continued, continuation

DEINIT

Roles: deinitiated, deinitiation

HABIT

Roles: habitual, habit

INIT

Roles: initiated, initiation

PREVENTION

Roles: prevented, prevention

TRANSIT

Roles: transitory, transition

REALITY

Roles: has-reality, reality

Used for predicates that say something about how true or certain the expression filling the has-reality role is.

Dependency graph for sentence: Passt das eh ?. Token Passt is labeled COMPARISON and has an edge labeled compared to token das and an edge labeled SCENE to token eh. Token eh is labeled REALITY and has an edge labeled has-reality to token Passt. Dependency graph for sentence: Kim probably knows that. Token probably is labeled REALITY and has an edge labeled has-reality to token knows. Token knows is labeled MESSAGE and has an edge labeled EXPERIENCER to token Kim and an edge labeled SCENE to token probably and an edge labeled topic to token that. Dependency graph for sentence: That 's really great. Token really is labeled REALITY and has an edge labeled has-reality to token great. Token great is labeled PROPERTY and has an edge labeled property to token That and an edge labeled SCENE to token really.

EXISTENCE

Roles: existent, existence

NECESSITY

Roles: necessary, necessity

NEG

Roles: negated, negation

This frame denotes negation. It can be used by itself or stacked onto META or other frames.

Dependency graph for sentence: They are not able to move. Token not is labeled NEG and has an edge labeled negated to token able. Token able is labeled META-POSSIBILITY and has an edge labeled participant to token They and an edge labeled scene to token move. Token move is labeled LOCATION-DYN and has an edge labeled has-location to token They. Dependency graph for sentence: absence of evidence. Token absence is labeled EXISTENCE-NEG and has an edge labeled existent to token evidence. Dependency graph for sentence: They never understand. Token never is labeled TIME-NEG and has an edge labeled has-time to token understand. Token understand is labeled MESSAGE and has an edge labeled EXPERIENCER to token They and an edge labeled SCENE to token never.

POSSIBILITY

Roles: possible, possibility

LITERALITY

Roles: has-literality, literality

PRECISION

Roles: has-precision, precision

QUANTITY

Roles: has-quantity, quantity

RANK

Roles: has-rank, rank

TIME

Roles: has-time, time

AGE

Roles: has-age, age

FREQUENCY

Roles: has-frequency, frequency

META

Roles: participant, scene

A “meta” frame for control predicates where the participant dependent is assigned a role by the scene dependent. This role must be annotated through an additional dependency from scene to participant or one of its dependents (a “backlink”).

Dependency graph for sentence: Kim finished their work. Token finished is labeled META-DEINIT and has an edge labeled participant to token Kim and an edge labeled scene to token work. Token work is labeled SITUATION-DYN and has an edge labeled situee to token Kim. Dependency graph for sentence: Kim refrained from going. Token refrained is labeled META-PREVENTION and has an edge labeled participant to token Kim. Token going is labeled LOCATION-INIT and has an edge labeled has-location to token Kim. Dependency graph for sentence: Kim prevented Sandy from going. Token prevented is labeled META-PREVENTION and has an edge labeled CAUSER to token Kim and an edge labeled participant to token Sandy. Token going is labeled LOCATION-INIT and has an edge labeled has-location to token Sandy. Dependency graph for sentence: Kim plays tennis. Token plays is labeled META and has an edge labeled participant to token Kim. Token tennis is labeled SITUATION-DYN and has an edge labeled situee to token Kim. Dependency graph for sentence: Kim used to play tennis. Token used is labeled META-HABIT-TIME and has an edge labeled participant to token Kim. Token play is labeled META and has an edge labeled participant to token Kim. Token tennis is labeled SITUATION-DYN and has an edge labeled situee to token Kim. Dependency graph for sentence: Winston machte nie den Versuch , das zu prüfen. Token machte is labeled META and has an edge labeled participant to token Winston and an edge labeled SCENE to token nie and an edge labeled scene to token Versuch. Token Versuch is labeled META-INIT and has an edge labeled participant to token Winston and an edge labeled scene to token prüfen. Token prüfen is labeled MESSAGE-INIT and has an edge labeled EXPERIENCER to token Winston and an edge labeled topic to token das.

It is possible for scene to assign two different dependents of the META predicate two different roles. The META predicate should assign both of them the participant role:

Dependency graph for sentence: Kim gave Sandy a kick. Token gave is labeled META and has an edge labeled participant to token Kim and an edge labeled participant to token Sandy and an edge labeled scene to token kick. Token kick is labeled CONTACT-INIT and has an edge labeled CAUSER to token Kim and an edge labeled surface to token Sandy. Dependency graph for sentence: Kim bekam Sandy zu fassen. Token bekam is labeled META-INIT and has an edge labeled participant to token Kim and an edge labeled participant to token Sandy and an edge labeled scene to token fassen. Token fassen is labeled POSSESSION and has an edge labeled possessor to token Kim and an edge labeled possessum to token Sandy.

On the other hand, META predicates may have arguments that are not members of the embedded scene, such as CAUSER:

Dependency graph for sentence: Kim made Sandy dance. Token made is labeled META-INIT and has an edge labeled CAUSER to token Kim and an edge labeled participant to token Sandy and an edge labeled scene to token dance. Token dance is labeled SITUATION-DYN and has an edge labeled situee to token Sandy.

The non-core role SCENE is used for adjectives, adverbs, and relative clauses, which also assign their syntactic head a role, annotated through a backlink:

Dependency graph for sentence: The clown I saw smiled. Token clown is labeled ENTITY and has an edge labeled SCENE to token saw. Token saw is labeled MESSAGE and has an edge labeled topic to token clown and an edge labeled EXPERIENCER to token I. Token smiled is labeled SENDING and has an edge labeled sender to token clown. Dependency graph for sentence: weit über das gesteckte Ziel hinausgehende Erfüllung. Token gesteckte is labeled NORM-INIT and has an edge labeled norm to token Ziel. Token Ziel is labeled ENTITY and has an edge labeled SCENE to token gesteckte. Token hinausgehende is labeled COMPARISON and has an edge labeled reference to token Ziel and an edge labeled compared to token Erfüllung. Token Erfüllung is labeled ENTITY and has an edge labeled SCENE to token hinausgehende. Dependency graph for sentence: Fortunately for Sandy , Kim is here. Token Fortunately is labeled EVALUATION and has an edge labeled EXPERIENCER to token Sandy and an edge labeled evaluee to token here. Token here is labeled LOCATION and has an edge labeled SCENE to token Fortunately and an edge labeled has-location to token Kim. Dependency graph for sentence: I devoted myself instead to geography. Token devoted is labeled OBLIGATION-INIT and has an edge labeled CAUSER to token I and an edge labeled obliged to token myself and an edge labeled SCENE to token instead and an edge labeled obliged-to to token geography. Token instead is labeled SUBSTITUTION and has an edge labeled substitute to token devoted.

MESSAGE

Roles: topic, message

A message about topic with content message exists in perceived, measured, or recorded form. When a message is created through expression or observation, use MESSAGE-INIT. When message and topic are both realized, content must assign a role to topic. Combine with SENDER and EXPERIENCER.

Use MESSAGE for predicates of expression if there is a topic and/or message argument (otherwise, use SENDING):

Dependency graph for sentence: Kim said : it ’s fine. Token said is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled message to token fine. Dependency graph for sentence: Kim said it was fine. Token said is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled message to token fine. Dependency graph for sentence: Kim called Sandy a liar. Token called is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled topic to token Sandy and an edge labeled message to token liar. Token liar is labeled SENDING-HABIT and has an edge labeled sender to token Sandy. Dependency graph for sentence: Kim told Sandy a secret. Token told is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled EXPERIENCER to token Sandy and an edge labeled message to token secret. Dependency graph for sentence: Kim talked about Sandy. Token talked is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled topic to token Sandy. Dependency graph for sentence: Kim talked shit about Sandy. Token talked is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled message to token shit and an edge labeled topic to token Sandy. Token shit is labeled MESSAGE and has an edge labeled topic to token Sandy.

Gesture is a kind of expression, too:

Dependency graph for sentence: Kim shook their head no. Token shook is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled NONCOMP to token head and an edge labeled message to token no.

What is depicted or talked/thought/etc. about gets the topic role:

Dependency graph for sentence: a picture of the heron. Token picture is labeled MESSAGE and has an edge labeled topic to token heron. Dependency graph for sentence: Kim drew the the heron. Token drew is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled topic to token heron. Dependency graph for sentence: history book. Token book is labeled MESSAGE and has an edge labeled topic to token history. Dependency graph for sentence: a book about the primeval forest. Token book is labeled MESSAGE and has an edge labeled topic to token forest.

The result of recording something gets the message role:

Dependency graph for sentence: The concert was recorded on tape. Token recorded is labeled MESSAGE-INIT and has an edge labeled topic to token concert and an edge labeled MEDIUM to token tape. Dependency graph for sentence: Kim drew a picture. Token drew is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled message to token picture. Dependency graph for sentence: Kim wrote Sandy a letter. Token wrote is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled EXPERIENCER to token Sandy and an edge labeled message to token letter. Dependency graph for sentence: Kim wrote the message onto a piece of paper with a pen in big red letters. Token wrote is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled message to token message and an edge labeled MEDIUM to token paper and an edge labeled MEANS to token pen and an edge labeled ACCOMPANIER to token letters. Token letters is labeled ENTITY and has an edge labeled SAME to token message. Dependency graph for sentence: The band recorded an album. Token recorded is labeled MESSAGE-INIT and has an edge labeled SENDER to token band and an edge labeled message to token album.

Use MESSAGE for predicates of perception if there is a topic and/or message argument (otherwise, use EXPERIENCE):

Dependency graph for sentence: Kim saw a flower. Token saw is labeled MESSAGE and has an edge labeled EXPERIENCER to token Kim and an edge labeled topic to token flower. Dependency graph for sentence: Kim found the flower beautiful. Token found is labeled MESSAGE and has an edge labeled EXPERIENCER to token Kim and an edge labeled topic to token flower and an edge labeled message to token beautiful. Token beautiful is labeled PROPERTY and has an edge labeled has-property to token flower. Dependency graph for sentence: Kim thinks Sandy is a liar. Token thinks is labeled MESSAGE and has an edge labeled EXPERIENCER to token Kim and an edge labeled message to token liar. Dependency graph for sentence: Kim thinks Sandy a liar. Token thinks is labeled MESSAGE and has an edge labeled EXPERIENCER to token Kim and an edge labeled topic to token Sandy and an edge labeled message to token liar. Token liar is labeled SENDING-HABIT and has an edge labeled sender to token Sandy. Dependency graph for sentence: Kim saw Sandy swim. Token saw is labeled MESSAGE and has an edge labeled EXPERIENCER to token Kim and an edge labeled topic to token Sandy and an edge labeled message to token swim. Token swim is labeled LOCATION-DYN and has an edge labeled has-location to token Sandy.

Predicates that denote the initiation of perception (e.g., by acquiring knowledge, or observation, or reasoning, use MESSAGE-INIT):

Dependency graph for sentence: The Thought Police observed Windston. Token observed is labeled MESSAGE-INIT and has an edge labeled EXPERIENCER to token Police and an edge labeled topic to token Windston. Dependency graph for sentence: Kim studied linguistics. Token studied is labeled MESSAGE-INIT and has an edge labeled EXPERIENCER to token Kim and an edge labeled topic to token linguistics. Dependency graph for sentence: Kim noticed the bird. Token noticed is labeled MESSAGE-INIT and has an edge labeled EXPERIENCER to token Kim and an edge labeled topic to token bird. Dependency graph for sentence: Kim taught Sandy Spanish. Token taught is labeled MESSAGE-INIT and has an edge labeled SENDER to token Kim and an edge labeled EXPERIENCER to token Sandy and an edge labeled topic to token Spanish. Dependency graph for sentence: Kim measured the elasticity. Token measured is labeled MESSAGE-INIT and has an edge labeled EXPERIENCER to token Kim and an edge labeled topic to token elasticity. Dependency graph for sentence: The jury found Kim guilty of the crime. Token found is labeled MESSAGE-INIT and has an edge labeled EXPERIENCER to token jury and an edge labeled topic to token Kim and an edge labeled message to token guilty. Token guilty is labeled META and has an edge labeled participant to token Kim and an edge labeled scene to token crime. Token crime is labeled ENTITY and has an edge labeled CAUSER to token Kim.

Predicates that denote the deinititation of perception use MESSAGE-DEINIT:

Dependency graph for sentence: Kim forgot everything they knew. Token forgot is labeled MESSAGE-DEINIT and has an edge labeled EXPERIENCER to token Kim and an edge labeled message to token everything. Dependency graph for sentence: Kim forgot about Sandy. Token forgot is labeled MESSAGE-DEINIT and has an edge labeled EXPERIENCER to token Kim and an edge labeled topic to token Sandy.

EVALUATION

Roles: evaluee, evaluation

Special case of MESSAGE that involves a value judgment or emotion about evaluee.

Dependency graph for sentence: The snake frightened them. Token frightened is labeled EVALUATION and has an edge labeled evaluee to token snake and an edge labeled EXPERIENCER to token them. Dependency graph for sentence: Kim disapproved of it. Token disapproved is labeled EVALUATION and has an edge labeled EXPERIENCER to token Kim and an edge labeled evaluee to token it. Dependency graph for sentence: Kim was pleased with Sandy. Token pleased is labeled EVALUATION and has an edge labeled EXPERIENCER to token Kim and an edge labeled evaluee to token Sandy.

NORM

Roles: norm-topic, norm

A special case of MESSAGE that does not make a descriptive claim, but is about how things should be. This includes wishes, orders, goals, etc.

Kim_SENDER *told#NORM-INIT* Sandy_norm-topic__sender to **call#SENDING**_norm
sales_norm-topic *target#NORM*
Kim_norm-subject__has-location *wants#NORM* to **swim#LOCATION-DYN**_norm
Kim_EXPERIENCER *wants#NORM* Sandy_norm-subject__has-location to **swim#LOCATION-DYN**_norm

RECORD

Roles: recorded, record

NONCOMP

Roles: has-noncomp, noncomp

This is a pseudo-frame that is used to mark syntactic dependents that are part of a multiword predicate – as in verbal idioms, weather verbs, existential there, names, other fixed expressions, or specialized sublanguages for such things as dates and times. (Light verbs, on the other hand, are treated with META).

Dependency graph for sentence: Kim kicked the bucket. Token kicked is labeled EXISTENCE-DEINIT and has an edge labeled existent to token Kim and an edge labeled NONCOMP to token bucket. Dependency graph for sentence: It is raining. Token raining is labeled ENTITY and has an edge labeled NONCOMP to token It. Dependency graph for sentence: There was a famine. Token was is labeled META and has an edge labeled NONCOMP to token There and an edge labeled scene to token famine.

Bound pronouns that are part of multiword expressions (e.g., inherently reflexive verbs) are annotated with the same role as their antecedent:

Dependency graph for sentence: Kim erinnert sich nicht daran. Token erinnert is labeled MESSAGE and has an edge labeled EXPERIENCER to token Kim and an edge labeled EXPERIENCER to token sich and an edge labeled SCENE to token nicht and an edge labeled topic to token daran. Token nicht is labeled NEG and has an edge labeled negated to token erinnert. Dependency graph for sentence: I address myself to you. Token address is labeled SENDING-INIT and has an edge labeled sender to token I and an edge labeled sender to token myself and an edge labeled EXPERIENCER to token you. Dependency graph for sentence: I had with me neither a mechanic nor any passenger. Token had is labeled ACCOMPANIMENT and has an edge labeled accompanied to token I and an edge labeled accompanied to token me and an edge labeled accompanier to token mechanic and an edge labeled accompanier to token passenger.

PERTINENCE

Roles: pertaining, pertained-to

SEQUENCE

Roles: follows, followed

ELABORATION

Roles: elaboration, elaborated-on

FULFILMENT

Roles: fulfilled, fulfiller

TIME-SEQUENCE

Roles: subsequent, precedent

REPETITION

Roles: repetition, original-occurrence

RESTORATION

Roles: restoration, original-state

SUBSTITUTION

Roles: replaced, substitute

SOCIAL-RELATION

Roles: socially-related, socially-related-to

OBLIGATION

Roles: obliged, obliged-to

MEMBERSHIP

Roles: member, social-group

Literal and Figurative Framing

Annotating Compounds

Annotating Exocentric Adverbs

Annotating Particle Verbs

In UD, particle verbs are connected to their particle via the compound:prt relation.

If the particle can be interpreted as an adposition with an elided complement (often the case with spatial meanings), label that relation as the elided complement would be labeled:

Dependency graph for sentence: get the lid off (the jar). Token get is labeled LOCATION-DEINIT and has an edge labeled has-location to token lid and an edge labeled location to token off. Dependency graph for sentence: You may go in (-to the room) now. Token go is labeled LOCATION-INIT and has an edge labeled has-location to token You and an edge labeled location to token in and an edge labeled SCENE to token now. Token now is labeled TIME and has an edge labeled has-time to token go.

Also treat separated and nonseparated adpositional adverbs this way:

Dependency graph for sentence: Komm herein !. Token Komm is labeled LOCATION-INIT and has an edge labeled location to token herein. Dependency graph for sentence: Geh da jetzt rein !. Token Geh is labeled LOCATION-INIT and has an edge labeled location to token da and an edge labeled SCENE to token jetzt and an edge labeled location to token rein. Token jetzt is labeled TIME and has an edge labeled has-time to token Geh.

Otherwise, use NONCOMP:

Dependency graph for sentence: eat up the cookies. Token eat is labeled CONTAINMENT-INIT and has an edge labeled NONCOMP to token up and an edge labeled contained to token cookies. Dependency graph for sentence: do somebody in. Token do is labeled SITUATION-INIT and has an edge labeled situee to token somebody and an edge labeled NONCOMP to token in. Dependency graph for sentence: Es stellte ein riesiges Gesicht dar. Token stellte is labeled MESSAGE and has an edge labeled message to token Es and an edge labeled topic to token Gesicht and an edge labeled NONCOMP to token dar.