DOCSLIB.ORG

Semantic Role Labeling for Learner Chinese

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read 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 nese can be. We find two non-obvious facts: this, we ask two senior students majoring in Ap- 1) the L1-sentence-trained systems performs plied Linguistics to carefully annotate some L2-L1 rather badly on the L2 data; 2) the performance parallel sentences with predicate–argument struc- drop from the L1 data to the L2 data of the tures according to the specification of Chinese two parser-based systems is much smaller, in- PropBank (CPB; Xue and Palmer, 2009), which dicating the importance of syntactic parsing in SRL for interlanguages. Finally, the pa- is developed for L1. A high inter-annotator agree- per introduces a new agreement-based model ment is achieved, suggesting the robustness of lan- to explore the semantic coherency informa- guage comprehension for L2. During the course of tion in the large-scale L2-L1 parallel data. We semantic annotation, we find a non-obvious fact then show such information is very effective that we can re-use the semantic annotation spec- to enhance SRL for learner texts. Our model ification, Chinese PropBank in our case, which is achieves an F-score of 72.06, which is a 2.02 developed for L1. Only modest rules are needed to point improvement over the best baseline. handle some tricky phenomena. This is quite dif- 1 Introduction ferent from syntactic treebanking for learner sen- tences, where defining a rich set of new annotation A learner language (interlanguage) is an idiolect heuristics seems necessary (Ragheb and Dickin- developed by a learner of a second or foreign son, 2012; Nagata and Sakaguchi, 2016; Berzak language which may preserve some features of et al., 2016). his/her first language. Previously, encouraging re- Our second concern is to mimic the human’s ro- sults of automatically building the syntactic anal- bust semantic processing ability by computer pro- ysis of learner languages were reported (Nagata grams. The feasibility of reusing the annotation and Sakaguchi, 2016), but it is still unknown how specification for L1 implies that we can reuse stan- semantic processing performs, while parsing a dard CPB data to train an SRL system to pro- learner language (L2) into semantic representa- cess learner texts. To test the robustness of the tions is the foundation of a variety of deeper anal- state-of-the-art SRL algorithms, we evaluate two ysis of learner languages, e.g., automatic essay types of SRL frameworks. The first one is a tradi- 1In this paper, we call sentences written by non-native tional SRL system that leverages a syntactic parser speakers (henceforth, “L2 sentences”), aligned to their cor- rections by native speakers (henceforth, “L1 sentences”) L2- and heavy feature engineering to obtain explicit L1 parallel sentences. information of semantic roles (Feng et al., 2012). 3793 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3793–3802 Brussels, Belgium, October 31 - November 4, 2018. c 2018 Association for Computational Linguistics Furthermore, we employ two different parsers for parallel dataset 2. comparison: 1) the PCFGLA-based parser, viz. Berkeley parser (Petrov et al., 2006), and 2) a min- 2 Semantic Analysis of An L2-L1 imal span-based neural parser (Stern et al., 2017). Parallel Corpus The other SRL system uses a stacked BiLSTM to implicitly capture local and non-local information 2.1 An L2-L1 Parallel Corpus (He et al., 2017). and we call it the neural syntax- An L2-L1 parallel corpus can greatly facilitate the agnostic system. All systems can achieve state-of- analysis of a learner language (Lee et al., 2017). the-art performance on L1 texts but show a signif- Following Mizumoto et al.(2011), we collected icant degradation on L2 texts. This highlights the a large dataset of L2-L1 parallel texts of Man- weakness of applying an L1-sentence-trained sys- darin Chinese by exploring “language exchange” tem to process learner texts. social networking services (SNS), i.e., Lang-8, a While the neural syntax-agnostic system ob- language-learning website where native speakers tains superior performance on the L1 data, the two can freely correct the sentences written by foreign syntax-based systems both produce better analy- learners. The proficiency levels of the learners are ses on the L2 data. Furthermore, as illustrated in diverse, but most of the learners, according to our the comparison between different parsers, the bet- judgment, is of intermediate or lower level. ter the parsing results we get, the better the perfor- Our initial collection consists of 1,108,907 sen- mance on L2 we achieve. This shows that syntac- tence pairs from 135,754 essays. As there is lots of tic parsing is important in semantic construction noise in raw sentences, we clean up the data by (1) for learner Chinese. The main reason, according ruling out redundant content, (2) excluding sen- to our analysis, is that the syntax-based system tences containing foreign words or Chinese pho- may generate correct syntactic analyses for par- netic alphabet by checking the Unicode values, (3) tial grammatical fragments in L2 texts, which pro- dropping overly simple sentences which may not vides crucial information for SRL. Therefore, syn- be informative, and (4) utilizing a rule-based clas- tactic parsing helps build more generalizable SRL sifier to determine whether to include the sentence models that transfer better to new languages, and into the corpus. enhancing syntactic parsing can improve SRL to The final corpus consists of 717,241 learner some extent. sentences from writers of 61 different native lan- guages, in which English and Japanese constitute Our last concern is to explore the potential of a the majority. As for completeness, 82.78% of the large-scale set of L2-L1 parallel sentences to en- Chinese Second Language sentences on Lang-8 hance SRL systems. We find that semantic struc- are corrected by native human annotators. One tures of the L2-L1 parallel sentences are highly sentence gets corrected approximately 1.53 times consistent. This inspires us to design a novel on average. agreement-based model to explore such seman- In this paper, we manually annotate the tic coherency information. In particular, we de- predicate–argument structures for the 600 L2-L1 fine a metric for comparing predicate–argument pairs as the basis for the semantic analysis of structures and searching for relatively good auto- learner Chinese. It is from the above corpus that matic syntactic and semantic annotations to ex- we carefully select 600 pairs of L2-L1 parallel tend the training data for SRL systems. Experi- sentences. We would choose the most appropri- ments demonstrate the value of the L2-L1 paral- ate one among multiple versions of corrections lel sentences as well as the effectiveness of our and recorrect the L1s if necessary. Because word method. We achieve an F-score of 72.06, which structure is very fundamental for various NLP is a 2.02 percentage point improvement over the tasks, our annotation also contains gold word seg- best neural-parser-based baseline. mentation for both L2 and L1 sentences. Note that To the best of our knowledge, this is the first there are no natural word boundaries in Chinese time that the L2-L1 parallel data is utilized to en- 2The data is collected from Lang-8 (www.lang-8. hance NLP systems for learner texts. com) and used as the training data in NLPCC 2018 Shared Task: Grammatical Error Correction (Zhao et al., 2018), For research purpose, we have released our SRL which can be downloaded at https://github.com/ annotations on 600 sentence pairs and the L2-L1 pkucoli/srl4il 3794 PRF tations selected by each annotator, discussing the L1 95.87 96.17 96.02 ENG differences, and either selecting one as fully cor- L2 94.78 93.06 93.91 rect or creating a hybrid representing the consen- L1 97.95 98.69 98.32 JPN sus decision for each choice point. When we felt L2 96.07 97.48 96.77 that the decisions were not already fully guided by L1 96.95 95.41 96.17 RUS the existing annotation guidelines, we worked to L2 97.04 94.08 95.53 articulate an extension to the guidelines that would L1 96.95 97.76 97.35 ARA support the decision.
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]
  • Semi-Supervised Learning for Spoken Language Understanding Using Semantic Role Labeling
    SEMI-SUPERVISED LEARNING FOR SPOKEN LANGUAGE UNDERSTANDING USING SEMANTIC ROLE LABELING Ananlada Chotimongkol Gokhan Tur Dilek Hakkani-Tur¨ Carnegie Mellon University AT&T Labs - Research Language Technologies Institute Florham Park, NJ, 07932 Pittsburgh, PA, 15213 gtur,dtur @research.att.com [email protected] ABSTRACT (ASR). Then, the intent (call-type) of the speaker is identi- fied from the recognized sequence, using a spoken language In a goal-oriented spoken dialog system, the major aim of understanding (SLU) component. Finally, the role of the di- language understanding is to classify utterances into one alog manager (DM) is to interact with the user in a natural or more of the pre-defined intents and extract the associ- way and help the user to achieve the task that the system is ated named entities. Typically, the intents are designed by designed to support. As an example, consider the utterance I a human expert according to the application domain. Fur- have a question about my bill. Assuming that the utterance thermore, these systems are trained using large amounts of is recognized correctly, the corresponding intent would be data manually labeled using an already prepared labeling Ask(Bill). Then the action that needs to be taken depends on guide. In this paper, we propose a semi-supervised spo- the DM. It may ask the user to further specify the problem ken language understanding approach based on the task- or route this call to the billing department. independent semantic role labeling of the utterances. The goal is to extract the predicates and the associated argu- For call-type classification, one can use a domain-de- ments from spoken language by using semantic role label- pendent statistical approach as in the previous work.
    [Show full text]