DOCSLIB.ORG

Using Ontology-Based Approaches to Representing Speech Transcripts for Automated Speech Scoring

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Using Ontology-Based Approaches to Representing Speech Transcripts for Automated Speech Scoring Syracuse University SURFACE School of Information Studies - Dissertations School of Information Studies (iSchool) 8-2013 Using Ontology-Based Approaches to Representing Speech Transcripts for Automated Speech Scoring Miao Chen Follow this and additional works at: https://surface.syr.edu/it_etd Part of the Library and Information Science Commons Recommended Citation Chen, Miao, "Using Ontology-Based Approaches to Representing Speech Transcripts for Automated Speech Scoring" (2013). School of Information Studies - Dissertations. 87. https://surface.syr.edu/it_etd/87 This Dissertation is brought to you for free and open access by the School of Information Studies (iSchool) at SURFACE. It has been accepted for inclusion in School of Information Studies - Dissertations by an authorized administrator of SURFACE. For more information, please contact [email protected]. ABSTRACT Text representation is a process of transforming text into some formats that computer systems can use for subsequent information-related tasks such as text classification. Representing text faces two main challenges: meaningfulness of representation and unknown terms. Research has shown evidence that these challenges can be resolved by using the rich semantics in ontologies. This study aims to address these challenges by using ontology-based representation and unknown term reasoning approaches in the context of content scoring of speech, which is a less explored area compared to some common ones such as categorizing text corpus (e.g. 20 newsgroups and Reuters). From the perspective of language assessment, the increasing amount of language learners taking second language tests makes automatic scoring an attractive alternative to human scoring for delivering rapid and objective scores of written and spoken test responses. This study focuses on the speaking section of second language tests and investigates ontology-based approaches to speech scoring. Most previous automated speech scoring systems for spontaneous responses of test takers assess speech by primarily using acoustic features such as fluency and pronunciation, while text features are less involved and exploited. As content is an integral part of speech, the study is motivated by the lack of rich text features in speech scoring and is designed to examine the effects of different text features on scoring performance. A central question to the study is how speech transcript content can be represented in an appropriate means for speech scoring. Previously used approaches from essay and speech scoring systems include bag-of-words and latent semantic analysis representations, which are adopted as baselines in this study; the experimental approaches are ontology-based, which can help improving meaningfulness of representation units and estimating importance of unknown terms. Two general domain ontologies, WordNet and Wikipedia, are used respectively for ontology-based representations. In addition to comparison between representation approaches, the author analyzes which parameter option leads to the best performance within a particular representation. The experimental results show that on average, ontology-based representations slightly enhances speech scoring performance on all measurements when combined with the bag-of-words representation; reasoning of unknown terms can increase performance on one measurement (cos.w4) but decrease others. Due to the small data size, the significance test (t-test) shows that the enhancement of ontology-based representations is inconclusive. The contributions of the study include: 1) it examines the effects of different representation approaches on speech scoring tasks; 2) it enhances the understanding of the mechanisms of representation approaches and their parameter options via in- depth analysis; 3) the representation methodology and framework can be applied to other tasks such as automatic essay scoring. USING ONTOLOGY-BASED APPROACHES TO REPRESENTING SPEECH TRANSCRIPTS FOR AUTOMATED SPEECH SCORING by Miao Chen B.S., Peking University, 2005 Dissertation Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Science and Technology. Syracuse University August 2013 Copyright © Miao Chen August 2013 All Rights Reserved ACKNOWLEDGEMENTS I owe many thanks to many people that helped me enormously through my dissertation: my advisor Prof. Jian Qin, who led me throughout the once tough time of my doctoral life, taught me to be a researcher, and put through a great deal of time on my thesis; my committee members professors Bei Yu and Howard Turtle, who provided me invaluable input not only to my dissertation but also my research in general; Dr. Klaus Zechner, to whom my special thanks go to, my mentor at Educational Testing Service when interned there, kindly provided data set from ETS, and discussed every detail of my thesis. I started my PhD work from 2005 with the goal of doing some research in information retrieval, while ending up in conducting research in natural language processing and ontology. I am pleased that the current work is not very far from my goal 8 years ago, and I really enjoy research in text analytics. During my thesis writing time, I received help from many people, in life and in research. Prof.Beth Plale, whom I worked for at Indiana University, generously allowed me enough time to finish up the thesis. Prof. Nancy McCracken has helped me in research for quite several years and I really appreciate her suggestions on my research. My PhD journey has been a long one, but it is a good journey and I learned many things. I especially thank my husband Xiaozhong Liu, without whose strong support and love this dissertation would have been impossible. I am grateful to my parents, who are always in upright spirit, and my parents in law, who helped take care of my daughter during the busiest dissertation writing time. Lastly, this is also dedicated to my daughter Tiana Liu, whose smile is the best gift when waking up in the morning. v CONTENT TABLE CHAPTER 1. INTRODUCTION ........................................................................... 1 1.1 REPRESENTATION IN INFORMATION SCIENCE: A GENERAL PERSPECTIVE ................................ 1 1.2 TEXT REPRESENTATION: OVERVIEW AND CHALLENGES .......................................................... 7 1.3 ONTOLOGY-BASED REPRESENTATION: A COMPLEMENT TO THE CHALLENGES ....................... 10 1.4 THE TEST BED: CONTENT SCORING OF SPEECH ................................................................... 12 1.5 RESEARCH FRAMEWORK AND DESIGN.................................................................................. 15 1.5.1 Conceptual Framework ............................................................................................... 15 1.5.2 Research Design ........................................................................................................ 16 1.6 RESEARCH QUESTIONS ....................................................................................................... 19 1.7 CONTRIBUTIONS AND IMPLICATIONS ..................................................................................... 21 CHAPTER 2 LITERATURE REVIEW ................................................................ 22 2.1 DOCUMENT REPRESENTATION IN GENERAL .......................................................................... 22 2.1.1 Bag of Words .............................................................................................................. 23 2.1.2 Latent Semantic Analysis ........................................................................................... 25 2.1.3 Other Representation Approaches ............................................................................. 30 2.1.4 Local and Global Representations .............................................................................. 32 2.1.5 Dimensionality Reduction ........................................................................................... 33 2.1.6 Document Representation in Essay Scoring .............................................................. 34 2.2 SECOND LANGUAGE ASSESSMENT AND AUTOMATED SCORING ............................................. 35 2.2.1 Theoretical Aspects .................................................................................................... 35 2.2.1.1 Overview ............................................................................................................................. 35 2.2.1.2 Speaking Proficiency ........................................................................................................... 38 2.2.2 Automated Scoring for Second Language Assessment ............................................. 41 2.2.2.1 Automated Essay Scoring ................................................................................................... 42 2.2.2.2 Automated Speech Scoring ................................................................................................ 45 2.3 ONTOLOGY AND ITS USE IN TEXT PROCESSING .................................................................... 48 2.3.1 Definitions of Ontology ................................................................................................ 48 2.3.2 Use in Text Processing ............................................................................................... 55 2.4 SUMMARY ........................................................................................................................... 61 CHAPTER 3. METHODOLOGY .......................................................................
Load more

Recommended publications
  • An Artificial Neural Network Approach for Sentence Boundary Disambiguation in Urdu Language Text
    The International Arab Journal of Information Technology, Vol. 12, No. 4, July 2015 395 An Artificial Neural Network Approach for Sentence Boundary Disambiguation in Urdu Language Text Shazia Raj, Zobia Rehman, Sonia Rauf, Rehana Siddique, and Waqas Anwar Department of Computer Science, COMSATS Institute of Information Technology, Pakistan Abstract: Sentence boundary identification is an important step for text processing tasks, e.g., machine translation, POS tagging, text summarization etc., in this paper, we present an approach comprising of Feed Forward Neural Network (FFNN) along with part of speech information of the words in a corpus. Proposed adaptive system has been tested after training it with varying sizes of data and threshold values. The best results, our system produced are 93.05% precision, 99.53% recall and 96.18% f-measure. Keywords: Sentence boundary identification, feed forwardneural network, back propagation learning algorithm. Received April 22, 2013; accepted September 19, 2013; published online August 17, 2014 1. Introduction In such conditions it would be difficult for a machine to differentiate sentence terminators from Sentence boundary disambiguation is a problem in natural language processing that decides about the ambiguous punctuations. beginning and end of a sentence. Almost all language Urdu language processing is in infancy stage and processing applications need their input text split into application development for it is way slower for sentences for certain reasons. Sentence boundary number of reasons. One of these reasons is lack of detection is a difficult task as very often ambiguous Urdu text corpus, either tagged or untagged. However, punctuation marks are used in the text.
    [Show full text]
  • Towards Interpretation As Natural Logic Abduction
    4Pin1-33 The 32nd Annual Conference of the Japanese Society for Artificial Intelligence, 2018 Towards Interpretation as Natural Logic Abduction Naoya Inoue∗1 Pontus Stenetorp∗2 Sebastian Riedel∗2 Kentaro Inui∗1∗3 ∗1 Tohoku University ∗2 University College London ∗3 RIKEN Center for Advanced Intelligence Project This paper studies an abductive reasoning framework on natural language representations. We show how to incorporate Natural Logic (McCartney et al. 2009) into Interpretation as Abduction (Hobbs et al. 1993). The resulting framework avoids the difficulty of natural language-formal representation conversion, while handling im- portant linguistic expressions such as negations and quantifiers. For proof-of-concept, we demonstrate that missing, implicit premises of arguments can be recovered by the proposed framework by a manual example walkthrough. 1. Introduction is orthogonal to the conventional formal representation- based approaches. More specifically, we study Natural Abduction is inference to the best explanation. This pa- Logic [Maccartney 09, Bowman 13, Angeli 14] in the con- per explores abduction for natural language understanding. text of abductive reasoning. Natural Logic is a framework Given a textual observation, the task is to generate the originally developed for entailment prediction. The advan- best, textual explanation. For example, the task of find- tage is that (i) it works on natural language expression di- ing a missing assumption of an argument [Habernal 17] is a rectly, and (ii) it supports important linguistic expressions special form of abduction. Suppose the following argument ∗1 such as negations and quantifiers. is given : We show how Natural Logic-based inference can be inte- (1) Claim: Immigration is really a problem.
    [Show full text]
  • A Comparative Study of Pretrained Language Models for Automated Essay Scoring with Adversarial Inputs
    A Comparative Study of Pretrained Language Models for Automated Essay Scoring with Adversarial Inputs 1st Phakawat Wangkriangkri 2nd Chanissara Viboonlarp 3rd Attapol T. Rutherford Department of Computer Engineering Department of Computer Engineering Department of Linguistics Chulalongkorn University Chulalongkorn University Chulalongkorn University Bangkok, Thailand Bangkok, Thailand Bangkok, Thailand [email protected] [email protected] [email protected] 4th Ekapol Chuangsuwanich Department of Computer Engineering Chulalongkorn University Bangkok, Thailand [email protected] Abstract—Automated Essay Scoring (AES) is a task that for scoring essays. We experimented three popular transfer- deals with grading written essays automatically without human learning text encoders: Global Vectors for Word Repre- intervention. This study compares the performance of three sentation (GloVe) [6], Embeddings from Language Models AES models which utilize different text embedding methods, namely Global Vectors for Word Representation (GloVe), Em- (ELMo) [7], and Bidirectional Encoder Representations from beddings from Language Models (ELMo), and Bidirectional Transformers (BERT) [8]. An ideal AES system should both Encoder Representations from Transformers (BERT). We used correlate with manually-graded essay scores and be resistant two evaluation metrics: Quadratic Weighted Kappa (QWK) and to adversarial hacks; thus, we evaluated the models against a novel “robustness”, which quantifies the models’ ability to both criteria. detect adversarial essays created by modifying normal essays to cause them to be less coherent. We found that: (1) the BERT- Our contributions can be summarized as follows: based model achieved the greatest robustness, followed by the • We developed three AES-specific models utilizing dif- GloVe-based and ELMo-based models, respectively, and (2) fine- ferent transfer-learning text encoders, and compared tuning the embeddings improves QWK but lowers robustness.
    [Show full text]
  • The Effects of Automated Essay Scoring As a High School Classroom Intervention
    UNLV Retrospective Theses & Dissertations 1-1-2008 The effects of automated essay scoring as a high school classroom intervention Kathie L Frost University of Nevada, Las Vegas Follow this and additional works at: https://digitalscholarship.unlv.edu/rtds Repository Citation Frost, Kathie L, "The effects of automated essay scoring as a high school classroom intervention" (2008). UNLV Retrospective Theses & Dissertations. 2839. http://dx.doi.org/10.25669/37p4-kdh7 This Dissertation is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself. This Dissertation has been accepted for inclusion in UNLV Retrospective Theses & Dissertations by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. THE EFFECTS OF AUTOMATED ESSAY SCORING AS A HIGH SCHOOL CLASSROOM INTERVENTION by Kathie L. Frost Bachelor of Science University of Arizona Tucson, Arizona Master of Business Administration University of Nevada, Las Vegas A dissertation submitted in partial fulfillment of the requirements for the Doctor of Philosophy Degree in Curriculum and Instruction Department of Curriculum and Instruction College of Education Graduate College University of Nevada, Las Vegas December 2008 UMI Number: 3352171 Copyright 2009 by Frost, Kathie L.
    [Show full text]
  • 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]
  • A Comparison of Knowledge Extraction Tools for the Semantic Web
    A Comparison of Knowledge Extraction Tools for the Semantic Web Aldo Gangemi1;2 1 LIPN, Universit´eParis13-CNRS-SorbonneCit´e,France 2 STLab, ISTC-CNR, Rome, Italy. Abstract. In the last years, basic NLP tasks: NER, WSD, relation ex- traction, etc. have been configured for Semantic Web tasks including on- tology learning, linked data population, entity resolution, NL querying to linked data, etc. Some assessment of the state of art of existing Knowl- edge Extraction (KE) tools when applied to the Semantic Web is then desirable. In this paper we describe a landscape analysis of several tools, either conceived specifically for KE on the Semantic Web, or adaptable to it, or even acting as aggregators of extracted data from other tools. Our aim is to assess the currently available capabilities against a rich palette of ontology design constructs, focusing specifically on the actual semantic reusability of KE output. 1 Introduction We present a landscape analysis of the current tools for Knowledge Extraction from text (KE), when applied on the Semantic Web (SW). Knowledge Extraction from text has become a key semantic technology, and has become key to the Semantic Web as well (see. e.g. [31]). Indeed, interest in ontology learning is not new (see e.g. [23], which dates back to 2001, and [10]), and an advanced tool like Text2Onto [11] was set up already in 2005. However, interest in KE was initially limited in the SW community, which preferred to concentrate on manual design of ontologies as a seal of quality. Things started changing after the linked data bootstrapping provided by DB- pedia [22], and the consequent need for substantial population of knowledge bases, schema induction from data, natural language access to structured data, and in general all applications that make joint exploitation of structured and unstructured content.
    [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]
  • Full-Text Processing: Improving a Practical NLP System Based on Surface Information Within the Context
    Full-text processing: improving a practical NLP system based on surface information within the context Tetsuya Nasukawa. IBM Research Tokyo Resem~hLaborat0ry t623-14, Shimotsurum~, Yimmt;0¢sl{i; I<almgawa<kbn 2421,, J aimn • nasukawa@t,rl:, vnet;::ibm icbm Abstract text. Without constructing a i):recige filodel of the eohtext through, deep sema~nfiCamtlys~is, our frmne= Rich information fl)r resolving ambigui- "work-refers .to a set(ff:parsed trees. (.r~sltlt~ 9 f syn- ties m sentence ~malysis~ including vari- : tacti(" miaiysis)ofeach sexitencd in t.li~;~i'ext as (:on- ous context-dependent 1)rol)lems. can be ob- text ilfformation, Thus. our context model consists tained by analyzing a simple set of parsed of parse(f trees that are obtained 1)y using mi exlst- ~rces of each senten('e in a text withom il!g g¢lwral syntactic parser. Excel)t for information constructing a predse model of the contex~ ()It the sequence of senl;,en('es, olIr framework does nol tl(rough deep senmntic.anMysis. Th.us. pro- consider any discourse stru(:~:ure mwh as the discourse cessmg• ,!,' a gloup" of sentem' .., '~,'(s togethel. i ': makes.' • " segmenm, focus space stack, or dominant hierarclty it.,p.(,)ss{t?!e .t.9 !~npl:ovel-t]le ~ccui'a~'Y (?f a :: :.it~.(.fi.ii~idin:.(cfi.0szufid, Sht/!er, dgs6)i.Tli6refbi.e, om< ,,~ ~-. ~t.ehi- - ....t sii~ 1 "~g;. ;, ~-ni~chin¢''ti-mslat{b~t'@~--..: .;- . .? • . : ...... - ~,',". ..........;-.preaches ":........... 'to-context pr0cessmg,,' • and.m" "tier-ran : ' le d at. •tern ; ::'Li:In i. thin.j.;'P'..p) a (r ~;.!iwe : .%d,es(tib~, ..i-!: ....
    [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]
  • Automated Ontology Creation Using XML Schema Elements
    Automated Ontology Creation using XML Schema Elements Samuel Suhas Singapogu, Paulo C. G. Costa, J. Mark Pullen C4I center, George Mason University Fairfax, VA, USA [ssingapo,pcosta,mpullen]@c4i.gmu.edu Abstract—Ontologies are commonly used to represent formal semantics in a computer system, usually capturing them in the Although ontologies are valuable assets in system model- form of concepts, relationships and axioms. Axioms convey ing, testing and analysis, the process of manually creating an asserted knowledge and support inferring new knowledge ontology for a complex system is inherently tedious and through logical reasoning. For complex systems, the process of error prone. Existing methods of knowledge discovery and creating ontologies manually can be tedious and error-prone. ontology creation usually are based on text mining of a data Many automated methods of knowledge discovery are based on mining domain text corpus, but current state-of-the-art meth- corpus for that domain. XML-based systems capture the ods using this approach fail to consider properly semantic data structure and syntax of all necessary and meaningful ele- embedded in XML schemata in complex systems. This paper ments in a XML schema, which therefore is a useful starting proposes a mapping method for identifying relevant semantic point for semantic analysis. In such systems, XML schemata data in XML schemata, automatically structuring and repre- have been shown to be a valuable resource of semantic data senting it in the form of a draft ontology. Concepts, concept [2]. Command and Control systems in the military context hierarchy and domain relationships from XML schema are support various functions, including commanding of forces mapped to relevant parts of an OWL ontology.
    [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]
  • Natural Language Processing
    Chowdhury, G. (2003) Natural language processing. Annual Review of Information Science and Technology, 37. pp. 51-89. ISSN 0066-4200 http://eprints.cdlr.strath.ac.uk/2611/ This is an author-produced version of a paper published in The Annual Review of Information Science and Technology ISSN 0066-4200 . This version has been peer-reviewed, but does not include the final publisher proof corrections, published layout, or pagination. Strathprints is designed to allow users to access the research output of the University of Strathclyde. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Users may download and/or print one copy of any article(s) in Strathprints to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profitmaking activities or any commercial gain. You may freely distribute the url (http://eprints.cdlr.strath.ac.uk) of the Strathprints website. Any correspondence concerning this service should be sent to The Strathprints Administrator: [email protected] Natural Language Processing Gobinda G. Chowdhury Dept. of Computer and Information Sciences University of Strathclyde, Glasgow G1 1XH, UK e-mail: [email protected] Introduction Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. NLP researchers aim to gather knowledge on how human beings understand and use language so that appropriate tools and techniques can be developed to make computer systems understand and manipulate natural languages to perform the desired tasks.
    [Show full text]