DOCSLIB.ORG

Neural Semantic Role Labeling Using Verb Sense Disambiguation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Neural Semantic Role Labeling Using Verb Sense Disambiguation Neural Semantic Role Labeling using Verb Sense Disambiguation Domenico Alfano Roberto Abbruzzese Donato Cappetta Eustema S.p.A. Eustema S.p.A. Eustema S.p.A. [email protected] [email protected] [email protected] Abstract study is the realization of a system able to perform SRL. The Natural Language Processing (NLP) A SRL system does nothing more than take a set community has recently experienced a of input phrases and, for each of them, it starts to growing interest in Semantic Role Label- determine the various components that could play ing (SRL). The increased availability of a semantic role. A component of a proposition annotated resources enables the develop- that plays a semantic role is defined as constituent. ment of statistical approaches specifically Once the possible candidates are determined, Ma- for SRL. This holds potential impact in chine Learning techniques are used to label them NLP applications. We examine and repro- with the right role. duce the Marcheggiani’s system and its in- This task becomes important for advanced appli- dividual components, including its anno- cations where it is also necessary to process the tated resources, parser, classification sys- semantic meaning of a sentence. Moreover, all this tem, the features used and the results ob- applications have to deal with ambiguity. tained by the system. Ambiguity is the term used to describe the fact Then, we explore different solutions in or- that a certain expression can be interpreted in more der to achieve better results by approach- than one way. ing to Verb-Sense Disambiguation (VSD). In NLP, ambiguity is present at several stages in VSD is a sub-problem of the Word Sense the processing of a text or a sentence, such as: Disambiguation (WSD) problem, that tries tokenization, sentence-splitting, part-of-speech to identify in which sense a polysemic (POS) tagging, syntactic parsing and semantic word is used in a given sentence. Thus a processing. Semantic ambiguity is usually the last sense inventory for each word (or lemma) to be addressed by NLP systems, and it tends to must be used. be one of the hardest to solve among all types of Finally, we also assess the challenges in ambiguities mentioned. SRL and identify the opportunities for use- For this type of ambiguity, the sentence has al- ful further research in future. ready been parsed and, even if its syntactic anal- ysis (parse tree) is unique and correct, some words 1 Introduction may feature more than one meaning for the gram- matical category they were tagged with. One of the fields where AI is gaining great impor- Usually this difference in meaning is associated to tance is the NLP. Nowadays, NLP has many ap- syntactic properties. In order to overcome these plications: search engines (semantic/topic search issues, this research study approaches to the VSD rather than word matching), automated speech task. The majority of the systems used in the VSD translation, automatic summarization, etc. task are based on Machine Learning techniques Therefore, there are many sub-tasks for natural (Witten, 2011). language applications that have already been stud- We approach both the tasks by following two dif- ied. An example is the syntactic analysis of the ferent solutions. words of a sentence. The object of this research Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 2 Related Work pared with vectors of features from testing examples using the cosine function. 2.1 SRL Approaches • Support Vector Machines, the most widely Until recently, state-of-the-art Semantic Role La- used classification technique in WSD at Sen- beling (SRL) systems relied on complex sets of sEval3 (Agirre, 2004); (Lee, 2004); (Vil- lexico-syntactic features (Pradhan, 2005) as well larejo, 2004), is a classification method that as declarative constraints (Punyakanok, 2008). finds the maximal margin hyperplane that Neural SRL models, instead, exploit induction ca- best separates the positive from the negative pabilities of neural networks, largely eliminating examples. In the particular case of WSD, the need for complex ”hand-made” features. Re- this has to be slightly tuned for multiple class cently, it has been shown that an accurate span- classification. Usually, methods like one- based SRL model can be constructed without re- against-all are used, which lead to the cre- lying on syntactic features (Jie Zhou, 2015). In ation of one classifier per class. particular, Roth and Lapata (Roth and Lapata, 2016) argue that syntactic features are necessary The most commonly used features used by the sys- for the dependency-based SRL and show that per- tems proposed and presented at SensEval3 can be formance of their model degrades dramatically if divided as follows: syntactic paths between arguments and predicates are not provided as an input. • Collocations: n-grams (usually bi-grams or Recent studies (Luheng He, 2018) propose an end- tri-grams) around the target word are col- to-end approach for jointly predicting all predi- lected. The information stored for then- cates, arguments spans, and the relations between grams is composed by the lemma, word-from them. The model makes independent decisions and part-of-speech tag of each word. about what relationship, if any, holds between ev- • Syntactic dependencies: syntactic dependen- ery possible word-span pair, and learns contextual- cies are extracted among words around the ized span representations that provide rich, shared target word. The relations most commonly input features for each decision. used are subject, object, modifier. However, depending on the system, other dependencies 2.2 WSD Approaches might also be extracted. An overview of the most used techniques and fea- tures for WSD was also conducted, based on the • Surrounding context: single words in a de- systems evaluated at the SensEval3. The most fined window size are extracted and used in a common learning algorithms (Witten, 2011) used bag-of-words approach. at SensEval3 are the following: • Knowledge-Based information: Some sys- tems also make use of information suchas • The Naive Bayes algorithm, which estimates WordNet’s domains, FrameNet’s syntactic the most probable sense for a given word w patterns or annotated examples, among oth- based on the prior probability of each sense ers. and the conditional probability for each of the features in that context. 3 Data • The Decision List algorithm (Yarowsky, The dataset used is the CoNLL 2009 Shared Task 1995), which builds a list of rules, ordered built on the CoNLL 2008 task which has been ex- from the highest to the lowest weighted fea- tended to multiple languages. The core of the task ture. The correct sense of the word is deter- was to predict syntactic and semantic dependen- mined by the first rule that is matched. cies and their labeling. Data was provided for both statistical training and • The Vector Space Model algorithm, which evaluation, in order to extract these labelled de- considers the features of the context as binary pendencies from manually annotated Treebanks values in a vector. In the training phase, a such as the Penn Treebank for English, the Prague centroid is calculated for each possible sense Dependency Treebank for Czech and similar Tree- of the word. These centroids are then com- banks for Catalan, Chinese, German, Japanese and Spanish languages, enriched with semantic rela- • A randomly initialized word embedding dw tions. Great effort has been dedicated in providing xre 2 R . the participants with a common and relatively sim- dw ple data representation for all the languages, simi- • A pre-trained word embedding xpe 2 R . lar to the 2008 English data. Role-annotated data • A randomly initialized part-of-speech tag makes it available for many research opportunities dp embedding xpos 2 R . in SRL including a broad spectrum of probabilis- tic and machine learning approaches. • A randomly initialized lemma embedding d We have introduced the dataset associated with xle 2 R l that is only active if the word is SRL; we are now prepared to discuss the ap- one of the predicates. proaches to automatic SRL and VBS. Then, it has been used the Predicate-Specific En- 4 Metrics coding. Specifically, when identifying arguments of a given predicate, the authors added a predicate- For many of these subtasks there are standard eval- specific feature to the representation of each word uations techniques and corpora. Standard eval- in the sentence by concatenating a binary flag to uation metrics from information retrieval include the word representation. The flag is set as 1 for precision, recall and a combined metric called F1 the word corresponding to the currently consid- measure (Jurafsky, 2000). ered predicate, it is set as 0 otherwise. In this way, Precision is a measure of how much of the infor- sentences with more than one predicate will be re- mation that the system returned is correct, also encoded by Bidirectional LSTMs multiple times. known as accuracy. Recall is a measure of how much relevant information the system has ex- 5.2 Encoder tracted from text, thus a measure of the system’s Recurrent neural networks (RNN) (Elman, coverage. The F1 measure balances recall and pre- 1990), more precisely, Long Short-Term Memory cision. (LSTM) networks (Hochreiter and Schmidhuber, A corpus is often divided into three sets: training 1997) are one of the most effective ways to model set, development set and testing set. Training set sequences. Formally, the LSTM is a function is used for training systems, whereas the develop- that takes as input the sequence and returns a ment set is used to tune parameters of the learning hidden state. This state can be regarded as a systems, and sselecting the best model.
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]
  • 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]
  • 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]
  • 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.
    [Show 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.
    [Show full text]
  • Semantic Role Labeling for Learner Chinese
    Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data Zi Lin123, Yuguang Duan3, Yuanyuan Zhao125, Weiwei Sun124 and Xiaojun Wan12 1Institute of Computer Science and Technology, Peking University 2The MOE Key Laboratory of Computational Linguistics, Peking University 3Department of Chinese Language and Literature, Peking University 4Center for Chinese Linguistics, Peking University 5Academy for Advanced Interdisciplinary Studies, Peking University fzi.lin,ariaduan,ws,[email protected], [email protected] Abstract scoring. In this paper, we study semantic pars- This paper studies semantic parsing for in- ing for interlanguage, taking semantic role label- terlanguage (L21), taking semantic role label- ing (SRL) as a case task and learner Chinese as a ing (SRL) as a case task and learner Chi- case language. nese as a case language. We first manu- Before discussing a computation system, we ally annotate the semantic roles for a set of first consider the linguistic competence and per- learner texts to derive a gold standard for formance. Can human robustly understand learner automatic SRL. Based on the new data, we texts? Or to be more precise, to what extent, a na- then evaluate three off-the-shelf SRL systems, i.e., the PCFGLA-parser-based, neural-parser- tive speaker can understand the meaning of a sen- based and neural-syntax-agnostic systems, to tence written by a language learner? Intuitively, gauge how successful SRL for learner Chi- the answer is towards the positive side. To validate
    [Show full text]