Unit 3: Available Corpora and Software
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Talk Bank: a Multimodal Database of Communicative Interaction
Talk Bank: A Multimodal Database of Communicative Interaction 1. Overview The ongoing growth in computer power and connectivity has led to dramatic changes in the methodology of science and engineering. By stimulating fundamental theoretical discoveries in the analysis of semistructured data, we can to extend these methodological advances to the social and behavioral sciences. Specifically, we propose the construction of a major new tool for the social sciences, called TalkBank. The goal of TalkBank is the creation of a distributed, web- based data archiving system for transcribed video and audio data on communicative interactions. We will develop an XML-based annotation framework called Codon to serve as the formal specification for data in TalkBank. Tools will be created for the entry of new and existing data into the Codon format; transcriptions will be linked to speech and video; and there will be extensive support for collaborative commentary from competing perspectives. The TalkBank project will establish a framework that will facilitate the development of a distributed system of allied databases based on a common set of computational tools. Instead of attempting to impose a single uniform standard for coding and annotation, we will promote annotational pluralism within the framework of the abstraction layer provided by Codon. This representation will use labeled acyclic digraphs to support translation between the various annotation systems required for specific sub-disciplines. There will be no attempt to promote any single annotation scheme over others. Instead, by promoting comparison and translation between schemes, we will allow individual users to select the custom annotation scheme most appropriate for their purposes. -
International Computer Science Institute Activity Report 2007
International Computer Science Institute Activity Report 2007 International Computer Science Institute, 1947 Center Street, Suite 600, Berkeley, CA 94704-1198 USA phone: (510) 666 2900 (510) fax: 666 2956 [email protected] http://www.icsi.berkeley.edu PRINCIPAL 2007 SPONSORS Cisco Defense Advanced Research Projects Agency (DARPA) Disruptive Technology Offi ce (DTO, formerly ARDA) European Union (via University of Edinburgh) Finnish National Technology Agency (TEKES) German Academic Exchange Service (DAAD) Google IM2 National Centre of Competence in Research, Switzerland Microsoft National Science Foundation (NSF) Qualcomm Spanish Ministry of Education and Science (MEC) AFFILIATED 2007 SPONSORS Appscio Ask AT&T Intel National Institutes of Health (NIH) SAP Volkswagen CORPORATE OFFICERS Prof. Nelson Morgan (President and Institute Director) Dr. Marcia Bush (Vice President and Associate Director) Prof. Scott Shenker (Secretary and Treasurer) BOARD OF TRUSTEES, JANUARY 2008 Prof. Javier Aracil, MEC and Universidad Autónoma de Madrid Prof. Hervé Bourlard, IDIAP and EPFL Vice Chancellor Beth Burnside, UC Berkeley Dr. Adele Goldberg, Agile Mind, Inc. and Pharmaceutrix, Inc. Dr. Greg Heinzinger, Qualcomm Mr. Clifford Higgerson, Walden International Prof. Richard Karp, ICSI and UC Berkeley Prof. Nelson Morgan, ICSI (Director) and UC Berkeley Dr. David Nagel, Ascona Group Prof. Prabhakar Raghavan, Stanford and Yahoo! Prof. Stuart Russell, UC Berkeley Computer Science Division Chair Mr. Jouko Salo, TEKES Prof. Shankar Sastry, UC Berkeley, Dean -
Collection of Usage Information for Language Resources from Academic Articles
Collection of Usage Information for Language Resources from Academic Articles Shunsuke Kozaway, Hitomi Tohyamayy, Kiyotaka Uchimotoyyy, Shigeki Matsubaray yGraduate School of Information Science, Nagoya University yyInformation Technology Center, Nagoya University Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan yyyNational Institute of Information and Communications Technology 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, 184-8795, Japan fkozawa,[email protected], [email protected], [email protected] Abstract Recently, language resources (LRs) are becoming indispensable for linguistic researches. However, existing LRs are often not fully utilized because their variety of usage is not well known, indicating that their intrinsic value is not recognized very well either. Regarding this issue, lists of usage information might improve LR searches and lead to their efficient use. In this research, therefore, we collect a list of usage information for each LR from academic articles to promote the efficient utilization of LRs. This paper proposes to construct a text corpus annotated with usage information (UI corpus). In particular, we automatically extract sentences containing LR names from academic articles. Then, the extracted sentences are annotated with usage information by two annotators in a cascaded manner. We show that the UI corpus contributes to efficient LR searches by combining the UI corpus with a metadata database of LRs and comparing the number of LRs retrieved with and without the UI corpus. 1. Introduction Thesaurus1. In recent years, such language resources (LRs) as corpora • He also employed Roget’s Thesaurus in 100 words of and dictionaries are being widely used for research in the window to implement WSD. -
A Resource of Corpus-Derived Typed Predicate Argument Structures for Croatian
CROATPAS: A Resource of Corpus-derived Typed Predicate Argument Structures for Croatian Costanza Marini Elisabetta Ježek University of Pavia University of Pavia Department of Humanities Department of Humanities costanza.marini01@ [email protected] universitadipavia.it Abstract 2014). The potential uses and purposes of the resource range from multilingual The goal of this paper is to introduce pattern linking between compatible CROATPAS, the Croatian sister project resources to computer-assisted language of the Italian Typed-Predicate Argument learning (CALL). Structure resource (TPAS1, Ježek et al. 2014). CROATPAS is a corpus-based 1 Introduction digital collection of verb valency structures with the addition of semantic Nowadays, we live in a time when digital tools type specifications (SemTypes) to each and resources for language technology are argument slot, which is currently being constantly mushrooming all around the world. developed at the University of Pavia. However, we should remind ourselves that some Salient verbal patterns are discovered languages need our attention more than others if following a lexicographical methodology they are not to face – to put it in Rehm and called Corpus Pattern Analysis (CPA, Hegelesevere’s words – “a steadily increasing Hanks 2004 & 2012; Hanks & and rather severe threat of digital extinction” Pustejovsky 2005; Hanks et al. 2015), (2018: 3282). According to the findings of initiatives such as whereas SemTypes – such as [HUMAN], [ENTITY] or [ANIMAL] – are taken from a the META-NET White Paper Series (Tadić et al. shallow ontology shared by both TPAS 2012; Rehm et al. 2014), we can state that and the Pattern Dictionary of English Croatian is unfortunately among the 21 out of 24 Verbs (PDEV2, Hanks & Pustejovsky official languages of the European Union that are 2005; El Maarouf et al. -
Student Research Workshop Associated with RANLP 2011, Pages 1–8, Hissar, Bulgaria, 13 September 2011
RANLPStud 2011 Proceedings of the Student Research Workshop associated with The 8th International Conference on Recent Advances in Natural Language Processing (RANLP 2011) 13 September, 2011 Hissar, Bulgaria STUDENT RESEARCH WORKSHOP ASSOCIATED WITH THE INTERNATIONAL CONFERENCE RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING’2011 PROCEEDINGS Hissar, Bulgaria 13 September 2011 ISBN 978-954-452-016-8 Designed and Printed by INCOMA Ltd. Shoumen, BULGARIA ii Preface The Recent Advances in Natural Language Processing (RANLP) conference, already in its eight year and ranked among the most influential NLP conferences, has always been a meeting venue for scientists coming from all over the world. Since 2009, we decided to give arena to the younger and less experienced members of the NLP community to share their results with an international audience. For this reason, further to the first successful and highly competitive Student Research Workshop associated with the conference RANLP 2009, we are pleased to announce the second edition of the workshop which is held during the main RANLP 2011 conference days on 13 September 2011. The aim of the workshop is to provide an excellent opportunity for students at all levels (Bachelor, Master, and Ph.D.) to present their work in progress or completed projects to an international research audience and receive feedback from senior researchers. We have received 31 high quality submissions, among which 6 papers have been accepted as regular oral papers, and 18 as posters. Each submission has been reviewed by -
MASC: the Manually Annotated Sub-Corpus of American English
MASC: The Manually Annotated Sub-Corpus of American English Nancy Ide*, Collin Baker**, Christiane Fellbaum†, Charles Fillmore**, Rebecca Passonneau†† *Vassar College Poughkeepsie, New York USA **International Computer Science Institute Berkeley, California USA †Princeton University Princeton, New Jersey USA ††Columbia University New York, New York USA E-mail: [email protected], [email protected], [email protected], [email protected], [email protected] Abstract To answer the critical need for sharable, reusable annotated resources with rich linguistic annotations, we are developing a Manually Annotated Sub-Corpus (MASC) including texts from diverse genres and manual annotations or manually-validated annotations for multiple levels, including WordNet senses and FrameNet frames and frame elements, both of which have become significant resources in the international computational linguistics community. To derive maximal benefit from the semantic information provided by these resources, the MASC will also include manually-validated shallow parses and named entities, which will enable linking WordNet senses and FrameNet frames within the same sentences into more complex semantic structures and, because named entities will often be the role fillers of FrameNet frames, enrich the semantic and pragmatic information derivable from the sub-corpus. All MASC annotations will be published with detailed inter-annotator agreement measures. The MASC and its annotations will be freely downloadable from the ANC website, thus providing -
Child Language
ABSTRACTS 14TH INTERNATIONAL CONGRESS FOR THE STUDY OF CHILD LANGUAGE IN LYON, IASCL FRANCE 2017 WELCOME JULY, 17TH21ST 2017 SPECIAL THANKS TO - 2 - SUMMARY Plenary Day 1 4 Day 2 5 Day 3 53 Day 4 101 Day 5 146 WELCOME! Symposia Day 2 6 Day 3 54 Day 4 102 Day 5 147 Poster Day 2 189 Day 3 239 Day 4 295 - 3 - TH DAY MONDAY, 17 1 18:00-19:00, GRAND AMPHI PLENARY TALK Bottom-up and top-down information in infants’ early language acquisition Sharon Peperkamp Laboratoire de Sciences Cognitives et Psycholinguistique, Paris, France Decades of research have shown that before they pronounce their first words, infants acquire much of the sound structure of their native language, while also developing word segmentation skills and starting to build a lexicon. The rapidity of this acquisition is intriguing, and the underlying learning mechanisms are still largely unknown. Drawing on both experimental and modeling work, I will review recent research in this domain and illustrate specifically how both bottom-up and top-down cues contribute to infants’ acquisition of phonetic cat- egories and phonological rules. - 4 - TH DAY TUESDAY, 18 2 9:00-10:00, GRAND AMPHI PLENARY TALK What do the hands tell us about lan- guage development? Insights from de- velopment of speech, gesture and sign across languages Asli Ozyurek Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands Most research and theory on language development focus on children’s spoken utterances. However language development starting with the first words of children is multimodal. Speaking children produce gestures ac- companying and complementing their spoken utterances in meaningful ways through pointing or iconic ges- tures. -
Lexical Ambiguity • Syntactic Ambiguity • Semantic Ambiguity • Pragmatic Ambiguity
Welcome to the course! IntroductionIntroduction toto NaturalNatural LanguageLanguage ProcessingProcessing (NLP)(NLP) Professors:Marta Gatius Vila Horacio Rodríguez Hontoria Hours per week: 2h theory + 1h laboratory Web page: http://www.cs.upc.edu/~gatius/engpln2017.html Main goal Understand the fundamental concepts of NLP • Most well-known techniques and theories • Most relevant existing resources • Most relevant applications NLP Introduction 1 Welcome to the course! IntroductionIntroduction toto NaturalNatural LanguageLanguage ProcessingProcessing Content 1. Introduction to Language Processing 2. Applications. 3. Language models. 4. Morphology and lexicons. 5. Syntactic processing. 6. Semantic and pragmatic processing. 7. Generation NLP Introduction 2 Welcome to the course! IntroductionIntroduction toto NaturalNatural LanguageLanguage ProcessingProcessing Assesment • Exams Mid-term exam- November End-of-term exam – Final exams period- all the course contents • Development of 2 Programs – Groups of two or three students Course grade = maximum ( midterm exam*0.15 + final exam*0.45, final exam * 0.6) + assigments *0.4 NLP Introduction 3 Welcome to the course! IntroductionIntroduction toto NaturalNatural LanguageLanguage ProcessingProcessing Related (or the same) disciplines: •Computational Linguistics, CL •Natural Language Processing, NLP •Linguistic Engineering, LE •Human Language Technology, HLT NLP Introduction 4 Linguistic Engineering (LE) • LE consists of the application of linguistic knowledge to the development of computer systems able to recognize, understand, interpretate and generate human language in all its forms. • LE includes: • Formal models (representations of knowledge of language at the different levels) • Theories and algorithms • Techniques and tools • Resources (Lingware) • Applications NLP Introduction 5 Linguistic knowledge levels – Phonetics and phonology. Language models – Morphology: Meaningful components of words. Lexicon doors is plural – Syntax: Structural relationships between words. -
From CHILDES to Talkbank
From CHILDES to TalkBank Brian MacWhinney Carnegie Mellon University MacWhinney, B. (2001). New developments in CHILDES. In A. Do, L. Domínguez & A. Johansen (Eds.), BUCLD 25: Proceedings of the 25th annual Boston University Conference on Language Development (pp. 458-468). Somerville, MA: Cascadilla. a similar article appeared as: MacWhinney, B. (2001). From CHILDES to TalkBank. In M. Almgren, A. Barreña, M. Ezeizaberrena, I. Idiazabal & B. MacWhinney (Eds.), Research on Child Language Acquisition (pp. 17-34). Somerville, MA: Cascadilla. Recent years have seen a phenomenal growth in computer power and connectivity. The computer on the desktop of the average academic researcher now has the power of room-size supercomputers of the 1980s. Using the Internet, we can connect in seconds to the other side of the world and transfer huge amounts of text, programs, audio and video. Our computers are equipped with programs that allow us to view, link, and modify this material without even having to think about programming. Nearly all of the major journals are now available in electronic form and the very nature of journals and publication is undergoing radical change. These new trends have led to dramatic advances in the methodology of science and engineering. However, the social and behavioral sciences have not shared fully in these advances. In large part, this is because the data used in the social sciences are not well- structured patterns of DNA sequences or atomic collisions in super colliders. Much of our data is based on the messy, ill-structured behaviors of humans as they participate in social interactions. Categorizing and coding these behaviors is an enormous task in itself. -
3 Corpus Tools for Lexicographers
Comp. by: pg0994 Stage : Proof ChapterID: 0001546186 Date:14/5/12 Time:16:20:14 Filepath:d:/womat-filecopy/0001546186.3D31 OUP UNCORRECTED PROOF – FIRST PROOF, 14/5/2012, SPi 3 Corpus tools for lexicographers ADAM KILGARRIFF AND IZTOK KOSEM 3.1 Introduction To analyse corpus data, lexicographers need software that allows them to search, manipulate and save data, a ‘corpus tool’. A good corpus tool is the key to a comprehensive lexicographic analysis—a corpus without a good tool to access it is of little use. Both corpus compilation and corpus tools have been swept along by general technological advances over the last three decades. Compiling and storing corpora has become far faster and easier, so corpora tend to be much larger than previous ones. Most of the first COBUILD dictionary was produced from a corpus of eight million words. Several of the leading English dictionaries of the 1990s were produced using the British National Corpus (BNC), of 100 million words. Current lexico- graphic projects we are involved in use corpora of around a billion words—though this is still less than one hundredth of one percent of the English language text available on the Web (see Rundell, this volume). The amount of data to analyse has thus increased significantly, and corpus tools have had to be improved to assist lexicographers in adapting to this change. Corpus tools have become faster, more multifunctional, and customizable. In the COBUILD project, getting concordance output took a long time and then the concordances were printed on paper and handed out to lexicographers (Clear 1987). -
Multimedia Corpora (Media Encoding and Annotation) (Thomas Schmidt, Kjell Elenius, Paul Trilsbeek)
Multimedia Corpora (Media encoding and annotation) (Thomas Schmidt, Kjell Elenius, Paul Trilsbeek) Draft submitted to CLARIN WG 5.7. as input to CLARIN deliverable D5.C3 “Interoperability and Standards” [http://www.clarin.eu/system/files/clarindeliverableD5C3_v1_5finaldraft.pdf] Table of Contents 1 General distinctions / terminology................................................................................................................................... 1 1.1 Different types of multimedia corpora: spoken language vs. speech vs. phonetic vs. multimodal corpora vs. sign language corpora......................................................................................................................................................... 1 1.2 Media encoding vs. Media annotation................................................................................................................... 3 1.3 Data models/file formats vs. Transcription systems/conventions.......................................................................... 3 1.4 Transcription vs. Annotation / Coding vs. Metadata ............................................................................................. 3 2 Media encoding ............................................................................................................................................................... 5 2.1 Audio encoding ..................................................................................................................................................... 5 2.2 -
The General Regionally Annotated Corpus of Ukrainian (GRAC, Uacorpus.Org): Architecture and Functionality
The General Regionally Annotated Corpus of Ukrainian (GRAC, uacorpus.org): Architecture and Functionality Maria Shvedova[0000-0002-0759-1689] Kyiv National Linguistic University [email protected] Abstract. The paper presents the General Regionally Annotated Corpus of Ukrainian, which is publicly available (GRAC: uacorpus.org), searchable online and counts more than 400 million tokens, representing most genres of written texts. It also features regional annotation, i. e. about 50 percent of the texts are attributed with regard to the different regions of Ukraine or countries of the di- aspora. If the author is known, the text is linked to their home region(s). The journalistic texts are annotated with regard to the place where the edition is pub- lished. This feature differs the GRAC from a majority of general linguistic cor- pora. Keywords: Ukrainian language, corpus, diachronic evolution, regional varia- tion. 1 Introduction Currently many major national languages have large universal corpora, known as “reference” corpora (cf. das Deutsche Referenzkorpus – DeReKo) or “national” cor- pora (a label dating back ultimately to the British National Corpus). These corpora are large, representative for different genres of written language, have a certain depth of (usually morphological and metatextual) annotation and can be used for many differ- ent linguistic purposes. The Ukrainian language lacks a publicly available linguistic corpus. Still there is a need of a corpus in the present-day linguistics. Independently researchers compile dif- ferent corpora of Ukrainian for separate research purposes with different size and functionality. As the community lacks a universal tool, a researcher may build their own corpus according to their needs.