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Frame-Semantic Role Labeling with Heterogeneous Annotations

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Frame-Semantic Role Labeling with Heterogeneous Annotations Frame-Semantic Role Labeling with Heterogeneous Annotations Meghana Kshirsagar∗ Sam Thomson∗ Nathan Schneider† Jaime Carbonell∗ Noah A. Smith∗ Chris Dyer∗ ∗School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA †School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK Abstract DESIRING: eager.a hope.n hope.v interested.a itch.v Experiencer Focal_participant Event want.v wish.n wish.v … do you want me to hold off until I finish July and August ? ACTIVITY_FINISH: complete.v We consider the task of identifying and la- FrameNet Agent Activity conclude.v finish.v … Agent End_point Desirable_action: ∅ HOLDING_OFF_ON: hold off.v beling the semantic arguments of a predi- wait.v cate that evokes a FrameNet frame. This DESIRING: eager.a hope.n Figure 1: Part of a sentence from FrameNethope.v interested.a full-text itch.v an- Experiencer Focal_participant Event task is challenging because there are only notation. 3 frames and their arguments arewant.v shown: wish.n wish.vDESIR … - do youA0 wantAM-ADV me to hold off until A1I finish July and August ? want-v-01 ING is evoked by want, ACTIVITY_FINISH by AfinishCTIVITY_FINISH, and: complete.vHOLD - a few thousand fully annotated sentences FrameNet Agent Activity conclude.v finish.v … theING people_OFF really_ON wantby holdus to stay off the. Thin course horizontal and finish the linesjob . representing PropBank Agent End_point Desirable_action: ∅ HOLDING_OFF_ON: hold off.v for supervised training. Our approach aug- argument spans areA0 labeled with role names.A1wait.v (Notfinish-v-01 shown: July ments an existing model with features de- and August evokeA1CALENDRICA3 _UNIT and fill its Unitstay-v-01role.) rived from FrameNet and PropBank and A0 AM-ADV A1 want-v-01 with partially annotated exemplars from the people really want us to stay the course and finish the job . PropBank FrameNet. We observe a 4% absolute in- A0 A1 finish-v-01 A1 A3 stay-v-01 crease in F1 versus the original model. Figure 2: A PropBank-annotated sentence from OntoNotes 1 Introduction (Hovy et al., 2006). The PB lexicon defines rolesets (verb sense–specific frames) and their core roles: e.g., finish-v-01 ‘cause to stop’, A0 ‘intentional agent’, A1 ‘thing finishing’, and A2 ‘explicit instrument, thing finished with’. (finish-v-03, by Paucity of data resources is a challenge for contrast, means ‘apply a finish, as to wood’.) Clear similarities semantic analyses like frame-semantic parsing to the FrameNet annotations in figure1 are evident, though PB uses lexical frames rather than deep frames and makes (Gildea and Jurafsky, 2002; Das et al., 2014) using some different decisions about roles (e.g., want-v-01 has no the FrameNet lexicon (Baker et al., 1998; Fillmore analogue to Focal_participant). and Baker, 2009).1 Given a sentence, a frame- • a PropBank-style SRL system, by using guide semantic parse maps word tokens to frames they features (§3.4).2 evoke, and for each frame, finds and labels its ar- gument phrases with frame-specific roles. An ex- These expansions of the training corpus and the ample appears in figure1. feature set for supervised argument identification In this paper, we address this argument iden- are integrated into SEMAFOR (Das et al., 2014), tification subtask, a form of semantic role label- the leading open-source frame-semantic parser ing (SRL), a task introduced by Gildea and Juraf- for English. We observe a 4% F1 improvement sky(2002) using an earlier version of FrameNet. in argument identification on the FrameNet test Our contribution addresses the paucity of annotated set, leading to a 1% F1 improvement on the full data for training using standard domain adaptation frame-semantic parsing task. Our code and mod- techniques. We exploit three annotation sources: els are available at http://www.ark.cs.cmu.edu/ SEMAFOR/. • the frame-to-frame relations in FrameNet, by using hierarchical features to share statistical 2 FrameNet strength among related roles (§3.2), • FrameNet’s corpus of partially-annotated ex- FrameNet represents events, scenarios, and rela- emplar sentences, by using “frustratingly tionships with an inventory of frames (such as easy” domain adaptation (§3.3), and 2Preliminary experiments training on PropBank annota- tions mapped to FrameNet via SemLink 1.2.2c (Bonial et al., ‡ Corresponding author: [email protected] 2013) hurt performance, likely due to errors and coverage 1 http://framenet.icsi.berkeley.edu gaps in the mappings. Full-Text Exemplars SHOPPING and SCARCITY). Each frame is associ- train test train test ated with a set of roles (or frame elements) called Sentences 2,780 2,420 137,515 4,132 to mind in order to understand the scenario, and Frames 15,019 4,458 137,515 4,132 lexical predicates (verbs, nouns, adjectives, and Overt arguments 25,918 7,210 278,985 8,417 adverbs) capable of evoking the scenario. For ex- TYPES ample, the BODY_MOVEMENT frame has Agent and Frames 642 470 862 562 Body_part as its core roles, and lexical entries in- Roles 2,644 1,420 4,821 1,224 Unseen frames vs. train: 46 0 cluding verbs such as bend, blink, crane, and curtsy, Roles in unseen frames vs. train: 178 0 plus the noun use of curtsy. In FrameNet 1.5, there Unseen roles vs. train: 289 38 are over 1,000 frames and 12,000 lexical predi- Unseen roles vs. combined train: 103 32 cates. Table 1: Characteristics of the training and test data. (These statistics exclude the development set, which contains 4,463 2.1 Hierarchy frames over 746 sentences.) The FrameNet lexicon is organized as a network, and arguments to as many words as possible. Be- with several kinds of frame-to-frame relations ginning with the SemEval-2007 shared task on linking pairs of frames and (subsets of) their ar- FrameNet analysis, frame-semantic parsers have guments (Ruppenhofer et al., 2010). In this work, been trained and evaluated on the full-text data 3 we consider two kinds of frame-to-frame relations: (Baker et al., 2007; Das et al., 2014). The full-text Inheritance: E.g., ROBBERY inherits from documents represent a mix of genres, prominently COMMITTING_CRIME, which inherits from MIS- including travel guides and bureaucratic reports DEED. Crucially, roles in inheriting frames about weapons stockpiles. are mapped to corresponding roles in inher- Exemplars: To document a given predicate, lexi- ited frames: ROBBERY.Perpetrator links to cographers manually select corpus examples and COMMITTING_CRIME.Perpetrator, which links to annotate them only with respect to the predicate MISDEED.Wrongdoer, and so forth. in question. These singly-annotated sentences Subframe: This indicates a subevent within a from FrameNet are called lexicographic exem- complex event. E.g., the CRIMINAL_PROCESS frame plars. There are over 140,000 sentences containing groups together subframes ARREST, ARRAIGN- argument annotations and relative to the FT dataset, MENT and TRIAL. CRIMINAL_PROCESS.Defendant, these contain an order of magnitude more frame for instance, is mapped to ARREST.Suspect, annotations and over two orders of magnitude more TRIAL.Defendant, and SENTENCING.Convict. sentences. As these were manually selected, the We say that a parent of a role is one that has rate of overt arguments per frame is noticeably either the Inheritance or Subframe relation to it. higher than in the FT data. The exemplars formed There are 4,138 Inheritance and 589 Subframe the basis of early studies of frame-semantic role links among role types in FrameNet 1.5. labeling (e.g., Gildea and Jurafsky, 2002; Thomp- Prior work has considered various ways of group- son et al., 2003; Fleischman et al., 2003; Litkowski, ing role labels together in order to share statisti- 2004; Kwon et al., 2004). Exemplars have not yet cal strength. Matsubayashi et al.(2009) observed been exploited successfully to improve role label- 4 small gains from using the Inheritance relation- ing performance on the more realistic FT task. ships and also from grouping by the role name 2.3 PropBank (SEMAFOR already incorporates such features). Johansson(2012) reports improvements in SRL for PropBank (PB; Palmer et al., 2005) is a lexicon and Swedish, by exploiting relationships between both corpus of predicate–argument structures that takes frames and roles. Baldewein et al.(2004) learn a shallower approach than FrameNet. FrameNet latent clusters of roles and role-fillers, reporting frames cluster lexical predicates that evoke sim- mixed results. Our approach is described in §3.2. 3Though these were annotated at the document level, 2.2 Annotations and train/development/test splits are by document, the frame- semantic parsing is currently restricted to the sentence level. Statistics for the annotations appear in table1. 4Das and Smith(2011, 2012) investigated semi-supervised Full-text (FT): This portion of the FrameNet cor- techniques using the exemplars and WordNet for frame iden- tification. Hermann et al.(2014) also improve frame iden- pus consists of documents and has about 5,000 tification by mapping frames and predicates into the same sentences for which annotators assigned frames continuous vector space, allowing statistical sharing. ilar kinds of scenarios In comparison, PropBank statistical generalizations about the kinds of argu- frames are purely lexical and there are no formal ments seen in those roles. Our hypothesis is that relations between different predicates or their roles. this will be beneficial given the small number of PropBank’s sense distinctions are generally coarser- training examples for individual roles. grained than FrameNet’s. Moreover, FrameNet lex- All roles that have a common parent based on ical entries cover many different parts of speech, the Inheritance and Subframe relations will share while PropBank focuses on verbs and (as of re- a set of features in common. Specifically, for each cently) eventive noun and adjective predicates. An base feature f which is conjoined with the role r example with PB annotations is shown in figure2.
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