DOCSLIB.ORG

Out-Of-Domain Framenet Semantic Role Labeling

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Out-Of-Domain Framenet Semantic Role Labeling Out-of-domain FrameNet Semantic Role Labeling Silvana Hartmann§†, Ilia Kuznetsov†, Teresa Martin§†, Iryna Gurevych§† Research Training Group AIPHES Ubiquitous§ Knowledge Processing (UKP) Lab Department† of Computer Science, Technische Universitat¨ Darmstadt http://www.ukp.tu-darmstadt.de Abstract on a range of benchmark datasets. This is crucial as the demand for semantic textual analysis of large- Domain dependence of NLP systems is one scale web data keeps growing. of the major obstacles to their application in large-scale text analysis, also restrict- Based on FrameNet (Fillmore et al., 2003), ing the applicability of FrameNet semantic FrameNet SRL extracts frame-semantic structures role labeling (SRL) systems. Yet, current on the sentence level that describe a specific FrameNet SRL systems are still only eval- situation centered around a semantic predicate, uated on a single in-domain test set. For often a verb, and its participants, typically the first time, we study the domain depen- syntactic arguments or adjuncts of the predicate. frame dence of FrameNet SRL on a wide range of The predicate is assigned a label, essentially benchmark sets. We create a novel test set a word sense label, that defines the situation and semantic roles for FrameNet SRL based on user-generated determines the of the participants. web text and find that the major bottleneck The following sentence from FrameNet provides Grinding for out-of-domain FrameNet SRL is the an example of the frame and its roles: frame identification step. To address this [The mill]Grinding cause grindsGrinding [the problem, we develop a simple, yet efficient malt]P atient [to grist]Result. system based on distributed word repre- sentations. Our system closely approaches FrameNet SRL consists of two steps, frame iden- the state-of-the-art in-domain while outper- tification (frameId), assigning a frame to the current forming the best available frame identifica- predicate, and role labeling (roleId), identifying the tion system out-of-domain. We publish our participants and assigning them role labels licensed system and test data for research purposes.1 by the frame. The frameId step reduces the hun- dreds of role labels in FrameNet to a manageable 1 Introduction set of up to 30 roles. Thus, FrameNet SRL dif- Domain dependence is a major problem for super- fers from PropBank SRL (Carreras and Marquez,` vised NLP tasks such as FrameNet semantic role 2005), that only uses a small set of 26 syntactically labeling (SRL): systems generally exhibit a strong motivated role labels and puts less weight on the performance drop when applied to test data from predicate sense. The advantage of FrameNet SRL a different distribution than the training data. This is that it results in a more fine-grained and rich prohibits their large-scale use in language technol- interpretation of the input sentences which is cru- ogy applications. cial for many applications, e.g. reasoning in online The same problems are expected for FrameNet debates (Berant et al., 2014). SRL, but due to a lack of datasets, state-of-the- Domain dependence is a well-studied topic for art FrameNet SRL is only evaluated on a single PropBank SRL. However, to the best of our knowl- in-domain test set, see e.g. Das et al. (2014) and edge, there exists no analysis of the performance FitzGerald et al. (2015). of modern FrameNet SRL systems when applied In this work, we present the first comprehensive to data from new domains. study of the domain dependence of FrameNet SRL In this work, we address this problem as fol- 1www.ukp.tu-darmstadt.de/ood-fn-srl lows: we introduce a new benchmark dataset YAGS 471 Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 471–482, Valencia, Spain, April 3-7, 2017. c 2017 Association for Computational Linguistics (Yahoo! Answers Gold Standard), which is based the corresponding CoNLL shared tasks promote on user-generated questions and answers and exem- out-of-domain evaluation (Surdeanu et al., 2008; plifies an out-of-domain application use case. We Hajicˇ et al., 2009). In the shared tasks, in-domain use YAGS, along with other out-of-domain test sets, newspaper text from the WSJ Corpus is contrasted to perform a detailed analysis of the domain depen- to out-of-domain data from fiction texts in the dence of FrameNet SRL using Semafor (Das et Brown Corpus. Most of the participants in the al., 2014; Kshirsagar et al., 2015) to identify which shared tasks do not consider domain adaptation of the stages of FrameNet SRL, frameId or roleId, and report systematically lower scores for the is particularly sensitive to domain shifts. Our re- out-of-domain data (Hajicˇ et al., 2009). sults confirm that the major bottleneck in FrameNet Representation learning has been successfully SRL is the frame identification step. Motivated used to improve on the CoNLL shared task re- by that, we develop a simple, yet efficient frame sults (Huang and Yates, 2010; FitzGerald et al., identification method based on distributed word 2015; Yang et al., 2015). Yang et al. (2015) re- representations that promise better domain gener- port the smallest performance difference (5.5 points alization. Our system’s performance matches the in F1) between in-domain and out-of-domain test state-of-the-art in-domain (Hermann et al., 2014), data, leading to the best results to date on the despite using a simpler model, and improves on the CoNLL 2009 out-of-domain test. Their system out-of-domain performance of Semafor. learns common representations for in-domain and The contributions of the present work are two- out-of-domain data based on deep belief networks. fold: 1) we perform the first comprehensive study of the domain generalization capabilities of open- Domain dependence of FrameNet SRL The source FrameNet SRL, and 2) we propose a new FrameNet 1.5 fulltext corpus, used as a standard frame identification method based on distributed dataset for training and evaluating FrameNet SRL word representations that enhances out-of-domain systems, contains texts from several domains (Rup- performance of frame identification. To enable our penhofer et al., 2010). However, the standard data study, we created YAGS, a new, substantially-sized split used to evaluate modern systems (Das and benchmark dataset for the out-of-domain testing of Smith, 2011) ensures the presence of all domains FrameNet SRL; we publish the annotations for the in the training as well as test data and cannot be YAGS benchmark set and our frame identification used to assess the systems’ ability to generalize. system for research purposes. Moreover, all the texts in the FrameNet fulltext corpus, based on newspaper and literary texts, are 2 Related work post-edited and linguistically well-formed. The FrameNet test setup thus cannot provide informa- The domain dependence of FrameNet SRL sys- tion on SRL performance on less edited out-of- tems has been only studied sparsely, however, there domain data, e.g. user-generated web data. exists a large body of work on out-of-domain Prop- There are few studies related to the out-of- Bank SRL, as well as on general domain adaptation domain generalization of FrameNet SRL. Johans- methods for NLP. This section briefly introduces son and Nugues (2008) evaluate the impact of dif- some of the relevant approaches in these areas, and ferent parsers on FrameNet SRL using the Nuclear then summarizes the state-of-the-art in FrameNet Threats Initiative (NTI) data as an out-of-domain frame identification. test set. They observe low domain generalization Domain adaptation in NLP Low out-of- abilities of their supervised system, but find that domain performance is a problem common to using dependency parsers instead of constituency many supervised machine learning tasks. The parsers is beneficial in the out-of-domain scenario. goal of domain adaptation is to improve model Croce et al. (2010) use a similar in-domain/out-of- performance on the test data originating from domain split to evaluate their approach to open- a different distribution than the training data domain FrameNet SRL. They integrate a distri- (Søgaard, 2013). For NLP, domain adaptation has butional model into their SRL system to general- been studied for various tasks such as POS-tagging ize lexicalized features to previously unseen argu- and syntactic parsing (Daume´ III, 2007; Blitzer ments and thus create an SRL system with a smaller et al., 2006). For the complex task of SRL, it performance gap between in-domain and out-of- is strongly associated with PropBank, because domain test data (only 4.5 percentage points F1). 472 Note that they only evaluate the role labeling step. The frame identification system of Semafor It is not transparent how their results would transfer relies on an elaborate feature set based on syntac- to the current state-of-the-art SRL systems that al- tic and lexical features, using the WordNet hierar- ready integrate methods to improve generalization, chy as a source of lexical information, and a label for instance using distributed representations. propagation-based approach to take unknown pred- Palmer and Sporleder (2010) analyze the icates into account. Semafor is not specifically FrameNet 1.3 training data coverage and the per- designed for out-of-domain use: the WordNet cov- formance of the Shalmaneser SRL system (Erk erage is limited, and the quality of syntactic parsing and Pado,´ 2006) for frame identification on sev- might drop when the system is applied to out-of- eral test sets across domains, i.e. the PropBank domain data, especially in case of non-standard and NTI parts of the FrameNet fulltext corpus and user-generated texts.
Load more

Recommended publications
  • Semantic Role Labeling 2
    Semantic Role Labeling 2 Outline • Semantic role theory • Designing semantic role annotation project ▫ Granularity ▫ Pros and cons of different role schemas ▫ Multi-word expressions 3 Outline • Semantic role theory • Designing semantic role annotation project ▫ Granularity ▫ Pros and cons of different role schemas ▫ Multi-word expressions 4 Semantic role theory • Predicates tie the components of a sentence together • Call these components arguments • [John] opened [the door]. 5 Discovering meaning • Syntax only gets you so far in answering “Who did what to whom?” John opened the door. Syntax: NPSUB V NPOBJ The door opened. Syntax: NPSUB V 6 Discovering meaning • Syntax only gets you so far in answering “Who did what to whom?” John opened the door. Syntax: NPSUB V NPOBJ Semantic roles: Opener REL thing opened The door opened. Syntax: NPSUB V Semantic roles: thing opened REL 7 Can the lexicon account for this? • Is there a different sense of open for each combination of roles and syntax? • Open 1: to cause something to become open • Open 2: become open • Are these all the senses we would need? (1) John opened the door with a crowbar. Open1? (2) They tried the tools in John’s workshop one after the other, and finally the crowbar opened the door. Still Open1? 8 Fillmore’s deep cases • Correspondence between syntactic case and semantic role that participant plays • “Deep cases”: Agentive, Objective, Dative, Instrument, Locative, Factitive • Loosely associated with syntactic cases; transformations result in the final surface case 9 The door opened. Syntax: NPSUB V Semantic roles: Objective REL John opened the door. Syntax: NPSUB V NPOBJ Semantic roles: Agentive REL Objective The crowbar opened the door.
    [Show full text]
  • ACL 2019 Social Media Mining for Health Applications (#SMM4H)
    ACL 2019 Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Proceedings of the Fourth Workshop August 2, 2019 Florence, Italy c 2019 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [email protected] ISBN 978-1-950737-46-8 ii Preface Welcome to the 4th Social Media Mining for Health Applications Workshop and Shared Task - #SMM4H 2019. The total number of users of social media continues to grow worldwide, resulting in the generation of vast amounts of data. Popular social networking sites such as Facebook, Twitter and Instagram dominate this sphere. According to estimates, 500 million tweets and 4.3 billion Facebook messages are posted every day 1. The latest Pew Research Report 2, nearly half of adults worldwide and two- thirds of all American adults (65%) use social networking. The report states that of the total users, 26% have discussed health information, and, of those, 30% changed behavior based on this information and 42% discussed current medical conditions. Advances in automated data processing, machine learning and NLP present the possibility of utilizing this massive data source for biomedical and public health applications, if researchers address the methodological challenges unique to this media. In its fourth iteration, the #SMM4H workshop takes place in Florence, Italy, on August 2, 2019, and is co-located with the
    [Show full text]
  • Enriching an Explanatory Dictionary with Framenet and Propbank Corpus Examples
    Proceedings of eLex 2019 Enriching an Explanatory Dictionary with FrameNet and PropBank Corpus Examples Pēteris Paikens 1, Normunds Grūzītis 2, Laura Rituma 2, Gunta Nešpore 2, Viktors Lipskis 2, Lauma Pretkalniņa2, Andrejs Spektors 2 1 Latvian Information Agency LETA, Marijas street 2, Riga, Latvia 2 Institute of Mathematics and Computer Science, University of Latvia, Raina blvd. 29, Riga, Latvia E-mail: [email protected], [email protected] Abstract This paper describes ongoing work to extend an online dictionary of Latvian – Tezaurs.lv – with representative semantically annotated corpus examples according to the FrameNet and PropBank methodologies and word sense inventories. Tezaurs.lv is one of the largest open lexical resources for Latvian, combining information from more than 300 legacy dictionaries and other sources. The corpus examples are extracted from Latvian FrameNet and PropBank corpora, which are manually annotated parallel subsets of a balanced text corpus of contemporary Latvian. The proposed approach augments traditional lexicographic information with modern cross-lingually interpretable information and enables analysis of word senses from the perspective of frame semantics, which is substantially different from (complementary to) the traditional approach applied in Latvian lexicography. In cases where FrameNet and PropBank corpus evidence aligns well with the word sense split in legacy dictionaries, the frame-semantically annotated corpus examples supplement the word sense information with clarifying usage examples and commonly used semantic valence patterns. However, the annotated corpus examples often provide evidence of a different sense split, which is often more coarse-grained and, thus, may help dictionary users to cluster and comprehend a fine-grained sense split suggested by the legacy sources.
    [Show full text]
  • Syntax Role for Neural Semantic Role Labeling
    Syntax Role for Neural Semantic Role Labeling Zuchao Li Hai Zhao∗ Shanghai Jiao Tong University Shanghai Jiao Tong University Department of Computer Science and Department of Computer Science and Engineering Engineering [email protected] [email protected] Shexia He Jiaxun Cai Shanghai Jiao Tong University Shanghai Jiao Tong University Department of Computer Science and Department of Computer Science and Engineering Engineering [email protected] [email protected] Semantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument struc- ture of a sentence. Previous studies in terms of traditional models have shown syntactic informa- tion can make remarkable contributions to SRL performance; however, the necessity of syntactic information was challenged by a few recent neural SRL studies that demonstrate impressive performance without syntactic backbones and suggest that syntax information becomes much less important for neural semantic role labeling, especially when paired with recent deep neural network and large-scale pre-trained language models. Despite this notion, the neural SRL field still lacks a systematic and full investigation on the relevance of syntactic information in SRL, for both dependency and both monolingual and multilingual settings. This paper intends to quantify the importance of syntactic information for neural SRL in the deep learning framework. We introduce three typical SRL frameworks (baselines), sequence-based, tree-based, and graph-based, which are accompanied by two categories of exploiting syntactic information: syntax pruning- based and syntax feature-based. Experiments are conducted on the CoNLL-2005, 2009, and 2012 benchmarks for all languages available, and results show that neural SRL models can still benefit from syntactic information under certain conditions.
    [Show full text]
  • Semantic Role Labeling Tutorial NAACL, June 9, 2013
    Semantic Role Labeling Tutorial NAACL, June 9, 2013 Part 1: Martha Palmer, University of Colorado Part 2: Shumin Wu, University of Colorado Part 3: Ivan Titov, Universität des Saarlandes 1 Outline } Part 1 Linguistic Background, Resources, Annotation Martha Palmer, University of Colorado } Part 2 Supervised Semantic Role Labeling and Leveraging Parallel PropBanks Shumin Wu, University of Colorado } Part 3 Semi- , unsupervised and cross-lingual approaches Ivan Titov, Universität des Saarlandes, Universteit van Amsterdam 2 Motivation: From Sentences to Propositions Who did what to whom, when, where and how? Powell met Zhu Rongji battle wrestle join debate Powell and Zhu Rongji met consult Powell met with Zhu Rongji Proposition: meet(Powell, Zhu Rongji) Powell and Zhu Rongji had a meeting meet(Somebody1, Somebody2) . When Powell met Zhu Rongji on Thursday they discussed the return of the spy plane. meet(Powell, Zhu) discuss([Powell, Zhu], return(X, plane)) Capturing semantic roles SUBJ } Dan broke [ the laser pointer.] SUBJ } [ The windows] were broken by the hurricane. SUBJ } [ The vase] broke into pieces when it toppled over. PropBank - A TreeBanked Sentence (S (NP-SBJ Analysts) S (VP have (VP been VP (VP expecting (NP (NP a GM-Jaguar pact) have VP (SBAR (WHNP-1 that) (S (NP-SBJ *T*-1) NP-SBJ been VP (VP would Analysts (VP give expectingNP (NP the U.S. car maker) SBAR (NP (NP an eventual (ADJP 30 %) stake) NP S (PP-LOC in (NP the British company)))))))))))) a GM-Jaguar WHNP-1 VP pact that NP-SBJ VP *T*-1 would NP give Analysts have been expecting a GM-Jaguar NP PP-LOC pact that would give the U.S.
    [Show full text]
  • Efficient Pairwise Document Similarity Computation in Big Datasets
    International Journal of Database Theory and Application Vol.8, No.4 (2015), pp.59-70 http://dx.doi.org/10.14257/ijdta.2015.8.4.07 Efficient Pairwise Document Similarity Computation in Big Datasets 1Papias Niyigena, 1Zhang Zuping*, 2Weiqi Li and 1Jun Long 1School of Information Science and Engineering, Central South University, Changsha, 410083, China 2School of Electronic and Information Engineering, Xi’an Jiaotong University Xian, 710049, China [email protected], * [email protected], [email protected], [email protected] Abstract Document similarity is a common task to a variety of problems such as clustering, unsupervised learning and text retrieval. It has been seen that document with the very similar content provides little or no new information to the user. This work tackles this problem focusing on detecting near duplicates documents in large corpora. In this paper, we are presenting a new method to compute pairwise document similarity in a corpus which will reduce the time execution and save space execution resources. Our method group shingles of all documents of a corpus in a relation, with an advantage of efficiently manage up to millions of records and ease counting and aggregating. Three algorithms are introduced to reduce the candidates shingles to be compared: one creates the relation of shingles to be considered, the second one creates the set of triples and the third one gives the similarity of documents by efficiently counting the shared shingles between documents. The experiment results show that our method reduces the number of candidates pairs to be compared from which reduce also the execution time and space compared with existing algorithms which consider the computation of all pairs candidates.
    [Show full text]
  • Cross-Lingual Semantic Role Labeling with Model Transfer Hao Fei, Meishan Zhang, Fei Li and Donghong Ji
    1 Cross-lingual Semantic Role Labeling with Model Transfer Hao Fei, Meishan Zhang, Fei Li and Donghong Ji Abstract—Prior studies show that cross-lingual semantic role transfer. The former is based on large-scale parallel corpora labeling (SRL) can be achieved by model transfer under the between source and target languages, where the source-side help of universal features. In this paper, we fill the gap of sentences are automatically annotated with SRL tags and cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods. then the source annotations are projected to the target-side We study both the bilingual transfer and multi-source transfer, sentences based on word alignment [7], [9], [10], [11], [12]. under gold or machine-generated syntactic inputs, pre-trained Annotation projection has received the most attention for cross- high-order abstract features, and contextualized multilingual lingual SRL. Consequently, a range of strategies have been word representations. Experimental results on the Universal proposed for enhancing the SRL performance of the target Proposition Bank corpus indicate that performances of the cross-lingual SRL can vary by leveraging different cross-lingual language, including improving the projection quality [13], joint features. In addition, whether the features are gold-standard learning of syntax and semantics information [7], iteratively also has an impact on performances. Precisely, we find that bootstrapping to reduce the influence of noise target corpus gold syntax features are much more crucial for cross-lingual [12], and joint translation-SRL [8]. SRL, compared with the automatically-generated ones.
    [Show full text]
  • Developing a Large Scale Framenet for Italian: the Iframenet Experience
    Developing a large scale FrameNet for Italian: the IFrameNet experience Roberto Basili° Silvia Brambilla§ Danilo Croce° Fabio Tamburini§ ° § Dept. of Enterprise Engineering Dept. of Classic Philology and Italian Studies University of Rome Tor Vergata University of Bologna {basili,croce}@info.uniroma2.it [email protected], [email protected] ian Portuguese, German, Spanish, Japanese, Swe- Abstract dish and Korean. All these projects are based on the idea that English. This paper presents work in pro- most of the Frames are the same among languages gress for the development of IFrameNet, a and that, thanks to this, it is possible to adopt large-scale, computationally oriented, lexi- Berkeley’s Frames and FEs and their relations, cal resource based on Fillmore’s frame se- with few changes, once all the language-specific mantics for Italian. For the development of information has been cut away (Tonelli et al. 2009, IFrameNet linguistic analysis, corpus- Tonelli 2010). processing and machine learning techniques With regard to Italian, over the past ten years are combined in order to support the semi- several research projects have been carried out at automatic development and annotation of different universities and Research Centres. In par- the resource. ticular, the ILC-CNR in Pisa (e.g. Lenci et al. 2008; Johnson and Lenci 2011), FBK in Trento (e.g. Italiano. Questo articolo presenta un work Tonelli et al. 2009, Tonelli 2010) and the Universi- in progress per lo sviluppo di IFrameNet, ty of Rome, Tor Vergata (e.g. Pennacchiotti et al. una risorsa lessicale ad ampia copertura, 2008, Basili et al.
    [Show full text]
  • Natural Language Semantic Construction Based on Cloud Database
    Copyright © 2018 Tech Science Press CMC, vol.57, no.3, pp.603-619, 2018 Natural Language Semantic Construction Based on Cloud Database Suzhen Wang1, Lu Zhang1, Yanpiao Zhang1, Jieli Sun1, Chaoyi Pang2, Gang Tian3 and Ning Cao4, * Abstract: Natural language semantic construction improves natural language comprehension ability and analytical skills of the machine. It is the basis for realizing the information exchange in the intelligent cloud-computing environment. This paper proposes a natural language semantic construction method based on cloud database, mainly including two parts: natural language cloud database construction and natural language semantic construction. Natural Language cloud database is established on the CloudStack cloud-computing environment, which is composed by corpus, thesaurus, word vector library and ontology knowledge base. In this section, we concentrate on the pretreatment of corpus and the presentation of background knowledge ontology, and then put forward a TF-IDF and word vector distance based algorithm for duplicated webpages (TWDW). It raises the recognition efficiency of repeated web pages. The part of natural language semantic construction mainly introduces the dynamic process of semantic construction and proposes a mapping algorithm based on semantic similarity (MBSS), which is a bridge between Predicate-Argument (PA) structure and background knowledge ontology. Experiments show that compared with the relevant algorithms, the precision and recall of both algorithms we propose have been significantly improved. The work in this paper improves the understanding of natural language semantics, and provides effective data support for the natural language interaction function of the cloud service. Keywords: Natural language, cloud database, semantic construction. 1 Introduction The Internet of things and big data have promoted the rapid development of the artificial intelligence industry.
    [Show full text]
  • Knowledge-Based Semantic Role Labeling Release 0.1
    Knowledge-based Semantic Role Labeling Release 0.1 September 11, 2014 Contents 1 Resources parsing 3 1.1 VerbNet parsing.............................................3 1.2 FrameNet parsing............................................4 2 Frames and arguments extractions7 2.1 Frame extraction.............................................7 2.2 Arguments extraction..........................................8 2.3 Evaluation................................................9 3 Debug ressources 11 3.1 Sample of headwords extractions.................................... 11 3.2 Dump module.............................................. 11 4 Evaluation 15 4.1 Initial mappings............................................. 15 4.2 Frame-matching............................................. 16 4.3 Probability model............................................ 16 4.4 Bootstrapping.............................................. 16 5 Ideas 17 5.1 Finding new matches........................................... 17 5.2 Restricting potential VerbNet classes.................................. 18 5.3 Informative probability model...................................... 18 6 Indices and tables 19 7 References 21 i ii Knowledge-based Semantic Role Labeling, Release 0.1 This repository contains: • A reproduction of (Swier & Stevenson, 2005) results for recent versions of resources and NLP tools (MST Parser, latest VerbNet, FrameNet and VN-FN mappings). • Some improvements of our own • Experiments on domain-specific knowledge-based semantic role labeling Documentation: Contents
    [Show full text]
  • Question-Answer Driven Semantic Role Labeling
    Question-Answer Driven Semantic Role Labeling Using Natural Language to Annotate Natural Language Luheng He, Mike Lewis, Luke Zettlemoyer University of Washington EMNLP 2015 1 Semantic Role Labeling (SRL) who did what to whom, when and where? 2 Semantic Role Labeling (SRL) Role Predicate Argument Agent Patent Time They increased the rent drastically this year Manner 2 Semantic Role Labeling (SRL) Role Predicate Argument Agent Patent Time They increased the rent drastically this year Manner • Defining a set of roles can be difficult • Existing formulations have used different sets 2 Existing SRL Formulations and Their Frame Inventories FrameNet PropBank 1000+ semantic frames, 10,000+ frame files 10,000+ frame elements (roles) with predicate-specific roles Frame: Change_position_on_a_scale This frame consists of words that indicate the Roleset Id: rise.01 , go up change of an Item's position on a scale Arg1-: Logical subject, patient, thing rising (the Attribute) from a starting point Arg2-EXT: EXT, amount risen (Initial_value) to an end point (Final_value). Arg3-DIR: start point The direction (Path) … Arg4-LOC: end point Lexical Units: Argm-LOC: medium …, reach.v, rise.n, rise.v, rocket.v, shift.n, … Unified Verb Index, University of Colorado http://verbs.colorado.edu/verb-index/ PropBank Annotation Guidelines, Bonial et al., 2010 FrameNet II: Extended theory and practice, Ruppenhofer et al., 2006 FrameNet: https://framenet.icsi.berkeley.edu/ 3 This Talk: QA-SRL • Introduce a new SRL formulation with no frame or role inventory • Use question-answer pairs to model verbal predicate-argument relations • Annotated over 3,000 sentences in weeks with non-expert, part-time annotators • Showed that this data is high-quality and learnable 4 Our Annotation Scheme Given sentence and a verb: They increased the rent this year .
    [Show full text]
  • Integrating Wordnet and Framenet Using a Knowledge-Based Word Sense Disambiguation Algorithm
    Integrating WordNet and FrameNet using a knowledge-based Word Sense Disambiguation algorithm Egoitz Laparra and German Rigau IXA group. University of the Basque Country, Donostia {egoitz.laparra,german.rigau}@ehu.com Abstract coherent groupings of words belonging to the same frame. This paper presents a novel automatic approach to In that way we expect to extend the coverage of FrameNet partially integrate FrameNet and WordNet. In that (by including from WordNet closely related concepts), to way we expect to extend FrameNet coverage, to en- enrich WordNet with frame semantic information (by port- rich WordNet with frame semantic information and ing frame information to WordNet) and possibly to extend possibly to extend FrameNet to languages other than FrameNet to languages other than English (by exploiting English. The method uses a knowledge-based Word local wordnets aligned to the English WordNet). Sense Disambiguation algorithm for linking FrameNet WordNet1 [12] (hereinafter WN) is by far the most lexical units to WordNet synsets. Specifically, we ex- ploit a graph-based Word Sense Disambiguation algo- widely-used knowledge base. In fact, WN is being rithm that uses a large-scale knowledge-base derived used world-wide for anchoring different types of seman- from WordNet. We have developed and tested four tic knowledge including wordnets for languages other than additional versions of this algorithm showing a sub- English [4], domain knowledge [17] or ontologies like stantial improvement over previous results. SUMO [22] or the EuroWordNet Top Concept Ontology [3]. It contains manually coded information about En- glish nouns, verbs, adjectives and adverbs and is organized around the notion of a synset.
    [Show full text]