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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. -
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 -
Conference Abstracts
EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION Held under the Patronage of Ms Neelie Kroes, Vice-President of the European Commission, Digital Agenda Commissioner MAY 23-24-25, 2012 ISTANBUL LÜTFI KIRDAR CONVENTION & EXHIBITION CENTRE ISTANBUL, TURKEY CONFERENCE ABSTRACTS Editors: Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Mehmet Uğur Doğan, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis. Assistant Editors: Hélène Mazo, Sara Goggi, Olivier Hamon © ELRA – European Language Resources Association. All rights reserved. LREC 2012, EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION Title: LREC 2012 Conference Abstracts Distributed by: ELRA – European Language Resources Association 55-57, rue Brillat Savarin 75013 Paris France Tel.: +33 1 43 13 33 33 Fax: +33 1 43 13 33 30 www.elra.info and www.elda.org Email: [email protected] and [email protected] Copyright by the European Language Resources Association ISBN 978-2-9517408-7-7 EAN 9782951740877 All rights reserved. No part of this book may be reproduced in any form without the prior permission of the European Language Resources Association ii Introduction of the Conference Chair Nicoletta Calzolari I wish first to express to Ms Neelie Kroes, Vice-President of the European Commission, Digital agenda Commissioner, the gratitude of the Program Committee and of all LREC participants for her Distinguished Patronage of LREC 2012. Even if every time I feel we have reached the top, this 8th LREC is continuing the tradition of breaking previous records: this edition we received 1013 submissions and have accepted 697 papers, after reviewing by the impressive number of 715 colleagues. -
Gold Standard Annotations for Preposition and Verb Sense With
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions Lori Moon Christos Christodoulopoulos Cynthia Fisher University of Illinois at Amazon Research University of Illinois at Urbana-Champaign [email protected] Urbana-Champaign [email protected] [email protected] Sandra Franco Dan Roth Intelligent Medical Objects University of Pennsylvania Northbrook, IL USA [email protected] [email protected] Abstract This paper describes the augmentation of an existing corpus of child-directed speech. The re- sulting corpus is a gold-standard labeled corpus for supervised learning of semantic role labels in adult-child dialogues. Semantic role labeling (SRL) models assign semantic roles to sentence constituents, thus indicating who has done what to whom (and in what way). The current corpus is derived from the Adam files in the Brown corpus (Brown, 1973) of the CHILDES corpora, and augments the partial annotation described in Connor et al. (2010). It provides labels for both semantic arguments of verbs and semantic arguments of prepositions. The semantic role labels and senses of verbs follow Propbank guidelines (Kingsbury and Palmer, 2002; Gildea and Palmer, 2002; Palmer et al., 2005) and those for prepositions follow Srikumar and Roth (2011). The corpus was annotated by two annotators. Inter-annotator agreement is given sepa- rately for prepositions and verbs, and for adult speech and child speech. Overall, across child and adult samples, including verbs and prepositions, the κ score for sense is 72.6, for the number of semantic-role-bearing arguments, the κ score is 77.4, for identical semantic role labels on a given argument, the κ score is 91.1, for the span of semantic role labels, and the κ for agreement is 93.9. -
(Or, the Raising of Baby Mondegreen) Dissertation
PRESERVING SUBSEGMENTAL VARIATION IN MODELING WORD SEGMENTATION (OR, THE RAISING OF BABY MONDEGREEN) DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Christopher Anton Rytting, B.A. ***** The Ohio State University 2007 Dissertation Committee: Approved by Dr. Christopher H. Brew, Co-Advisor Dr. Eric Fosler-Lussier, Co-Advisor Co-Advisor Dr. Mary Beckman Dr. Brian Joseph Co-Advisor Graduate Program in Linguistics ABSTRACT Many computational models have been developed to show how infants break apart utterances into words prior to building a vocabulary—the “word segmenta- tion task.” Most models assume that infants, upon hearing an utterance, represent this input as a string of segments. One type of model uses statistical cues calcu- lated from the distribution of segments within the child-directed speech to locate those points most likely to contain word boundaries. However, these models have been tested in relatively few languages, with little attention paid to how different phonological structures may affect the relative effectiveness of particular statistical heuristics. This dissertation addresses this is- sue by comparing the performance of two classes of distribution-based statistical cues on a corpus of Modern Greek, a language with a phonotactic structure signif- icantly different from that of English, and shows how these differences change the relative effectiveness of these cues. Another fundamental issue critically examined in this dissertation is the practice of representing input as a string of segments. Such a representation im- plicitly assumes complete certainty as to the phonemic identity of each segment. -
Metapragmatics of Playful Speech Practices in Persian
To Be with Salt, To Speak with Taste: Metapragmatics of Playful Speech Practices in Persian Author Arab, Reza Published 2021-02-03 Thesis Type Thesis (PhD Doctorate) School School of Hum, Lang & Soc Sc DOI https://doi.org/10.25904/1912/4079 Copyright Statement The author owns the copyright in this thesis, unless stated otherwise. Downloaded from http://hdl.handle.net/10072/402259 Griffith Research Online https://research-repository.griffith.edu.au To Be with Salt, To Speak with Taste: Metapragmatics of Playful Speech Practices in Persian Reza Arab BA, MA School of Humanities, Languages and Social Science Griffith University Thesis submitted in fulfilment of the requirements of the Degree of Doctor of Philosophy September 2020 Abstract This investigation is centred around three metapragmatic labels designating valued speech practices in the domain of ‘playful language’ in Persian. These three metapragmatic labels, used by speakers themselves, describe success and failure in use of playful language and construe a person as pleasant to be with. They are hāzerjavāb (lit. ready.response), bāmaze (lit. with.taste), and bānamak (lit. with.salt). Each is surrounded and supported by a cluster of (related) word meanings, which are instrumental in their cultural conceptualisations. The analytical framework is set within the research area known as ethnopragmatics, which is an offspring of Natural Semantics Metalanguage (NSM). With the use of explications and scripts articulated in cross-translatable semantic primes, the metapragmatic labels and the related clusters are examined in meticulous detail. This study demonstrates how ethnopragmatics, its insights on epistemologies backed by corpus pragmatics, can contribute to the metapragmatic studies by enabling a robust analysis using a systematic metalanguage. -
Neuroinformatics.Pdf
1 23 Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be self- archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy Neuroinform DOI 10.1007/s12021-013-9210-5 ORIGINAL ARTICLE Action and Language Mechanisms in the Brain: Data, Models and Neuroinformatics Michael A. Arbib & James J. Bonaiuto & Ina Bornkessel-Schlesewsky & David Kemmerer & Brian MacWhinney & Finn Årup Nielsen & Erhan Oztop # Springer Science+Business Media New York 2013 Abstract We assess the challenges of studying action and Databasing empirical data . Federation of databases . language mechanisms in the brain, both singly and in relation Collaboratory workspaces to each other to provide a novel perspective on neuroinformatics, integrating the development of databases for encoding – separately or together – neurocomputational models and Bridging the Gap Between Models and Experiments empirical data that serve systems and cognitive neuroscience. The present article offers a perspective on the neuroinformatics Keywords Linking models and experiments . Models, challenges of linking neuroscience data with models of the neurocomputational . -
Unit 3: Available Corpora and Software
Corpus building and investigation for the Humanities: An on-line information pack about corpus investigation techniques for the Humanities Unit 3: Available corpora and software Irina Dahlmann, University of Nottingham 3.1 Commonly-used reference corpora and how to find them This section provides an overview of commonly-used and readily available corpora. It is also intended as a summary only and is far from exhaustive, but should prove useful as a starting point to see what kinds of corpora are available. The Corpora here are divided into the following categories: • Corpora of General English • Monitor corpora • Corpora of Spoken English • Corpora of Academic English • Corpora of Professional English • Corpora of Learner English (First and Second Language Acquisition) • Historical (Diachronic) Corpora of English • Corpora in other languages • Parallel Corpora/Multilingual Corpora Each entry contains the name of the corpus and a hyperlink where further information is available. All the information was accurate at the time of writing but the information is subject to change and further web searches may be required. Corpora of General English The American National Corpus http://www.americannationalcorpus.org/ Size: The first release contains 11.5 million words. The final release will contain 100 million words. Content: Written and Spoken American English. Access/Cost: The second release is available from the Linguistic Data Consortium (http://projects.ldc.upenn.edu/ANC/) for $75. The British National Corpus http://www.natcorp.ox.ac.uk/ Size: 100 million words. Content: Written (90%) and Spoken (10%) British English. Access/Cost: The BNC World Edition is available as both a CD-ROM or via online subscription. -
English Corpus Linguistics: an Introduction - Charles F
Cambridge University Press 0521808790 - English Corpus Linguistics: An Introduction - Charles F. Meyer Index More information Index Aarts, Bas, 4, 102 Biber, Douglas, et al. (1999) 14 adequacy, 2–3, 10–11 Birmingham Corpus, 15, 142 age, 49–50 Blachman, Edward, 76–7 Altenberg, Bengt, 26–7 BNC see British National Corpus AMALGAM Tagging Project, 86–7, 89 Brill, Eric, 86 American National Corpus, 24, 84, 142 Brill Tagger, 86–8 American Publishing House for the Blind British National Corpus (BNC), 143 Corpus, 17, 142 annotation, 84 analyzing a corpus, 100 composition, 18, 31t, 34, 36, 38, 40–1, 49 determining suitability, 103–7, 107t copyright, 139–40 exploring a corpus, 123–4 planning, 30–2, 33, 43, 51, 138 extracting information: defining parameters, record keeping, 66 107–9; coding and recording, 109–14, research using, 15, 36 112t; locating relevant constructions, speech samples, 59 114–19, 116f, 118f tagging, 87 framing research question, 101–3 time-frame, 45 future prospects, 140–1 British National Corpus (BNC) Sampler, see also pseudo-titles (corpus analysis 139–40, 143 case study); statistical analysis Brown Corpus, xii, 1, 143 anaphors, 97 genre variation, 18 annotation, 98–9 length, 32 future prospects, 140 research using, 6, 9–10, 12, 42, 98, 103 grammatical markup, 81 sampling methodology, 44 parsing, 91–6, 98, 140 tagging, 87, 90 part-of-speech markup, 81 time-frame, 45 structural markup, 68–9, 81–6 see also FROWN (Freiburg–Brown) tagging, 86–91, 97–8, 111, 117–18, 140 Corpus types, 81 Burges, Jen, 52 appositions, 42, 98 Burnard, -
The Phonetic Analysis of Speech Corpora
The Phonetic Analysis of Speech Corpora Jonathan Harrington Institute of Phonetics and Speech Processing Ludwig-Maximilians University of Munich Germany email: [email protected] Wiley-Blackwell 2 Contents Relationship between International and Machine Readable Phonetic Alphabet (Australian English) Relationship between International and Machine Readable Phonetic Alphabet (German) Downloadable speech databases used in this book Preface Notes of downloading software Chapter 1 Using speech corpora in phonetics research 1.0 The place of corpora in the phonetic analysis of speech 1.1 Existing speech corpora for phonetic analysis 1.2 Designing your own corpus 1.2.1 Speakers 1.2.2 Materials 1.2.3 Some further issues in experimental design 1.2.4 Speaking style 1.2.5 Recording setup 1.2.6 Annotation 1.2.7 Some conventions for naming files 1.3 Summary and structure of the book Chapter 2 Some tools for building and querying labelling speech databases 2.0 Overview 2.1 Getting started with existing speech databases 2.2 Interface between Praat and Emu 2.3 Interface to R 2.4 Creating a new speech database: from Praat to Emu to R 2.5 A first look at the template file 2.6 Summary 2.7 Questions Chapter 3 Applying routines for speech signal processing 3.0 Introduction 3.1 Calculating, displaying, and correcting formants 3.2 Reading the formants into R 3.3 Summary 3.4 Questions 3.5 Answers Chapter 4 Querying annotation structures 4.1 The Emu Query Tool, segment tiers and event tiers 4.2 Extending the range of queries: annotations from the same -
Two Approaches to Metaphor Detection Brian Macwhinney and Davida Fromm Carnegie Mellon University Pittsburgh PA, 15213 USA E-Mail: [email protected], [email protected]
Two Approaches to Metaphor Detection Brian MacWhinney and Davida Fromm Carnegie Mellon University Pittsburgh PA, 15213 USA E-mail: [email protected], [email protected] Abstract Methods for automatic detection and interpretation of metaphors have focused on analysis and utilization of the ways in which metaphors violate selectional preferences (Martin, 2006). Detection and interpretation processes that rely on this method can achieve wide coverage and may be able to detect some novel metaphors. However, they are prone to high false alarm rates, often arising from imprecision in parsing and supporting ontological and lexical resources. An alternative approach to metaphor detection emphasizes the fact that many metaphors become conventionalized collocations, while still preserving their active metaphorical status. Given a large enough corpus for a given language, it is possible to use tools like SketchEngine (Kilgariff, Rychly, Smrz, & Tugwell, 2004) to locate these high frequency metaphors for a given target domain. In this paper, we examine the application of these two approaches and discuss their relative strengths and weaknesses for metaphors in the target domain of economic inequality in English, Spanish, Farsi, and Russian. Keywords: metaphor, corpora, grammatical relations, selectional restrictions, collocations • Action_Has_Modifier: verb-adverb pairs. 1. The Task (trap softly) Entity_Has_Modifier: noun-adjective pairs. In our project, code-named METAL, the task was to • (grinding poverty) automatically detect metaphors in the target domain Entity_Entity: noun-noun pairs. (Kövecses, 2002) of economic inequality – a subject of • particular current interest in the wake of the financial (wealth ladder) Entity_Has_Entity: noun-noun pairs for possession. collapse of 2008 and growing income disparity within • (poverty s trap, burden of poverty) and between countries caused by globalization and ’ Entity_Is_Entity: noun-noun pairs for identity. -
Computational Modeling of Human Language Acquisition Copyright © 2011 by Morgan & Claypool
SeriesSeries ISSN: ISSN: 1947-4040 1947-4040 ALISHAHI ALISHAHI ALISHAHI SSYNTHESISYNTHESIS LLECTURESECTURES ONON MM MorganMorgan& & ClaypoolClaypool PublishersPublishers HHUMANUMAN L LANGUAGEANGUAGE T TECHNOLOGIESECHNOLOGIES &&CC SeriesSeries Editor: Editor: Graeme Graeme Hirst, Hirst, University University of of Toronto Toronto ComputationalComputational Modeling Modeling of of Human Human Language Language Acquisition Acquisition COMPUTATIONAL MODELING OF HUMAN LANGUAGE ACQUISITION COMPUTATIONAL MODELING COMPUTATIONAL OF MODELING HUMAN OF LANGUAGE HUMAN ACQUISITION LANGUAGE ACQUISITION AfraAfra Alishahi, Alishahi, University University of of Saarlandes Saarlandes ComputationalComputational ModelingModeling HumanHuman language language acquisition acquisition has has been been studied studied for for centuries, centuries, but but using using computational computational modeling modeling for for such such studiesstudiesstudies is isis a a arelatively relativelyrelatively recent recentrecent trend. trend.trend. However, However,However, computational computationalcomputational approaches approachesapproaches to toto language languagelanguage learning learninglearning have havehave become becomebecome ofof HumanHuman LanguageLanguage increasinglyincreasinglyincreasingly popular, popular,popular, mainly mainlymainly due duedue to toto advances advancesadvances in inin developing developingdeveloping machine machinemachine learning learninglearning techniques, techniques,techniques, and andand the thethe availability availabilityavailability