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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. -
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 -
Implicitness of Discourse Relations
Implicitness of Discourse Relations F atemeh T orabi Asr and V era Demberg Cluster of Excellence Multimodal Computing and Interaction (MMCI) Saarland University Campus C7.4, !"# Saarbrüc&en, Germany [email protected], [email protected] Abstract The annotations of explicit and implicit discourse connectives in the Penn Discourse Treebank make it possible to investigate on a large scale how different types of discourse relations are expressed. Assuming an account of the Uniform Information Density hypothesis we expect that discourse relations should be expressed explicitly with a discourse connector when they are unexpected, but may be implicit when the discourse relation can be anticipated. !e investigate whether discourse relations which have been argued to be expected by the comprehender exhibit a higher ratio of implicit connectors. !e "nd support for two hypotheses put forth in previous research which suggest that continuous and causal relations are presupposed by language users when processing consecutive sentences in a text. !e then proceed to analyze the effect of Implicit Causality (IC) verbs (which have been argued to raise an expectation for an explanation) as a local cue for an upcoming causal relation. Keywords: Causality Continuity Discourse relations, Discourse cues Implicit discourse relations, Corpus study Uniform Information Density. Proceedings of COLING 2012: Technical Papers, pages 2669–2684, COLING 2012, Mumbai, December 2012. 2669 1 Introduction David Hume in his prominent work “An enquiry concerning human understanding” proposed that ideas in the human mind were associated according to at least three types of relations: resemblance contiguity in time or place, and causality (Hume, 1784). -
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. -
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. -
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. -
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. -
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. -
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 -
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 . -
Discourse Relations and Defeasible Knowledge`
DISCOURSE RELATIONS AND DEFEASIBLE KNOWLEDGE* Alex Lascarides t Nicholas Asher Human Communication Research Centre Center for Cognitive Science University of Edinburgh University of Texas 2 Buccleuch Place, Edinburgh, EH8 9LW Austin, Texas 78712 Scotland USA alex@uk, ac. ed. cogsc£ asher@sygmund, cgs. utexas, edu Abstract of the event described in a, i.e. fl's event is part of the preparatory phase of a's: 2 This paper presents a formal account of the temporal interpretation of text. The distinct nat- (2) The council built the bridge. The architect ural interpretations of texts with similar syntax drew up the plans. are explained in terms of defeasible rules charac- terising causal laws and Gricean-style pragmatic Explanation(a, fl): For example the event de- maxims. Intuitively compelling patterns of defea,- scribed in clause fl caused the event described in sible entailment that are supported by the logic clause a: in which the theory is expressed are shown to un- derly temporal interpretation. (3) Max fell. John pushed him. Background(a, fl): For example the state de- The Problem scribed in fl is the 'backdrop' or circumstances under which the event in a occurred (so the event and state temporally overlap): The temporal interpretation of text involves an account of how the events described are related (4) Max opened the door. The room was pitch to each other. These relations follow from the dark. discourse relations that are central to temporal import. 1 Some of these are listed below, where Result(a, fl): The event described in a caused the clause a appears in the text before fl: the event or state described in fl: Narration(a,fl): The event described in fl is a consequence of (but not necessarily caused by) (5) Max switched off the light. -
Facing the Most Difficult Case of Semantic Role Labeling: A
Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training Quynh Ngoc Thi Do1, Steven Bethard2, Marie-Francine Moens1 1Katholieke Universiteit Leuven, Belgium 2University of Arizona, United States [email protected] [email protected] [email protected] Abstract We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited. In this work, co-training is used together with word embeddings to improve the performance of a system trained on CoNLL 2009 training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results. 1 Introduction Semantic role labeling (SRL) is an essential natural language processing (NLP) task that identifies the relations between a predicate and its arguments in a given sentence. Intuitively, it aims at answering the questions of “Who did What to Whom, and How, When and Where?” in text. For example, the processing of the sentence “He bought tons of roses yesterday” should result in the identification of a “buying” event corresponding to the predicate “bought” with three arguments including “he” as the Agent (A0), “tons of roses” as the Thing being bought (A1), and “yesterday” as the Time (AM-TMP) arguments.