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ISCApad #181 |
| Wednesday, July 10, 2013 by Chris Wellekens |
7 Journals
| 7-1 | EURASIP Journal Special Issue on Informed Acoustic Source Separation
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| 7-2 | EURASIP Journal on Advances in Signal Processing:*Special Issue on Informed Acoustic Source Separation*
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| 7-3 | International Journal of Computational Linguistics and Chinese Language Processing: Special Issue on Processing Lexical Tones in Natural Speech
International Journal of Computational Linguistics and Chinese Language Processing Special Issue on Processing Lexical Tones in Natural Speech
This special issue aims to address questions about how lexical tones are processed by humans and machines in the context of natural, continuous speech. Lexical tones in tone languages have been widely investigated in the fields of linguistics, psycholinguistics, computational linguistics, and language acquisition by applying a wide range of theoretical, empirical, and experimental approaches. As the phonetic representation of lexical tones which are produced in connected speech can differ considerably from that of lexical tones which are produced in isolation, research interests constantly grow in how lexical tones are produced, perceived, and processed in realistic speech data. This special issue aims to bring together methodologies from different research disciplines to extend our understanding of lexical tones used in real speaking situations. We welcome submissions addressing the following issues.
Paper submission deadline: J Notification of acceptance: May 31, 2013 Final paper due: August 31, 2013 Tentative publication date: December, 2013 All submitted papers should present original research work, which has not been published elsewhere. Submitted manuscripts will be peer-reviewed by at least two independent reviewers. For detailed submission guidelines, please visit the website of the International Journal of Computational Linguistics and Chinese Language Processingat http://www.aclclp.org.tw/journal/submit.php. Please also feel free to contact the Guest Editor of this special issue, Dr. Shu-Chuan Tseng, at tsengsc@gate.sinica.edu.tw, if you need any additional information.
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| 7-4 | Numéro spécial de la revue TAL sur 'les Entités Nommées et leurs relations'PREMIER APPEL À CONTRIBUTIONS
Numéro spécial de la revue TAL sur 'les Entités Nommées et leurs relations'
Direction : Sophia Ananiadou (Sophia.ananiadou@manchester.ac.uk)
Nathalie Friburger (nathalie.friburger@univ-tours.fr)
Sophie Rosset (sophie.rosset@limsi.fr)
Les entités nommées constituent un champ de recherche très actif depuis de
nombreuses années. Elles sont depuis longtemps considérées comme un point
central dans de nombreuses applications mettant en jeu des notions comme la
compréhension, la recherche sémantique, etc. La notion d'Entités Nommées (EN)
couvre non seulement les noms propres mais aussi des entités plus complexes
comme les expressions multi-mots. Les entités nommées sont en général typées
selon des taxonomies plus ou moins vastes et fortement dépendantes du domaine
d'application ou des besoins considérés. Elles recouvrent classiquement des noms
désignant des personnes, des lieux ou des organisations mais peuvent aussi se
rapporter à des notions plus techniques comme les maladies. La détection des entités
et de leurs relations, malgré de nombreuses années de recherche, reste un problème
difficile. Les principaux points à résoudre incluent la résolution des ambiguïtés,
la détection des synonymes, la co-référence et la variabilité (acronymes, orthographe etc.).
Plusieurs méthodes ont été proposées et évaluées pour améliorer la détection et la
classification des entités ainsi que leurs relations. Ces méthodes vont des approches
fondées sur des connaissances explicites (approches à base de règles, de lexiques, etc.)
aux approches à base d'apprentissage supervisé, faiblement supervisé voire non supervisé.
L'évaluation des systèmes de détection des entités nécessite au minimum l'existence de
corpus de référence (gold standard). L'évaluation des relations ajoute un niveau de
complexité puisqu'il s'agit, le plus souvent, d'une tâche complexe impliquant la détection
des entités puis des relations entre elles. Comment évaluer la détection des relations dans
le cadre d'une tâche complète ? Quelle métrique utiliser pour tenir compte des erreurs de la
tâche précédente (détection des EN) ? Si les entités nommées simples permettent
d'atteindre de bons voire de très bons résultats, il n'en va pas de même lorsque leur
définition est complexe ou lorsqu'on traite des domaines spécialisés. Nous invitons donc
les contributions portant sur tout aspect relatif au traitement des entités nommées, et
en particulier (liste non exclusive) : - définition et typologie des entités nommées, y
compris dans un sens étendu - détection des entités nommées et type de documents
(résumés, articles, ressources collaboratives comme Wikipedia, domaine de spécialité,
media sociaux comme twitter, emails, file de discussion, parole...) - détection des empans
et analyse structurelle des EN - co-référence inter-documents et suivi d'entités - suivi
d'entités à travers le temps, les groupes sociaux et géographiques, suivi d'entités intra-
et inter-documents, etc. - reconnaissance d'entités nommées dans le domaine général
ou en domaine de spécialité - guides et schémas d'annotation, outils, méthodes et
corpus annotés - aspects multilingues, extraction d'entités dans des corpus comparables
ou parallèles - désambiguïsation des entités - applications et entités - évaluation,
comparaison et validation d'outils LANGUE Les articles sont écrits en français ou en anglais.
Les soumissions en anglais ne sont acceptées que pour les auteurs non francophones.
LA REVUE Depuis 40 ans, TAL (Traitement Automatique des Langues) est un journal
international publié par l'ATALA (Association pour le Traitement Automatique des Langues)
avec le soutien du CNRS. Depuis quelques années, il s'agit d'un journal en ligne, des
versions papier pouvant être obtenues sur commande. Ceci n'affecte en rien le processus
de relecture et de sélection.
DATES IMPORTANTES
* 15 avril 2013 Deadline pour la soumission * Juillet 2013 Notification aux auteurs
* Automne 2013 Publication FORMAT DE LA SOUMISSION Les articles soumis doivent
décrire un travail original, complet et non publié. Chaque soumission sera étudiée par
2 membres du comité de programme. Les articles (25 pages environ, format PDF)
doivent être déposés sur la plateforme Sciencesconf [adresse disponible très bientôt]
Merci de contacter les éditeurs de cette revue si vous souhaitez y soumettre un article
en leur fournissant un résumé d'une page.
Sophia Ananiadou (Sophia.ananiadou@manchester.ac.uk)
Nathalie Friburger (nathalie.friburger@univ-tours.fr)
Sophie Rosset (sophie.rosset@limsi.fr)
Les papiers acceptés feront au maximum 25 pages en PDF.
Le style est disponible pour téléchargement sur le site du
journal (http://www.atala.org/-Revue-TAL-)
COMITE SCIENTIFIQUE (tentative)
Maud Ehrmann, European Commission, JRC
Olivier Galibert, LNE, France
Natalia Grabar, STL, Université de Lille 1 et 3, France
Kais Haddar, University of Sfax, Tunisie
Thierry Hamon, LIM&Bio, Paris 13, France
Sanda Harabagiu, Texas, USA
Valia Kordoni, Humboldt-Universität, Berlin, Germany
Anna Korhonen, University of Cambridge, UK
Ioannis Korkontzelos, University of Manchester, UK
Anne-Laure Ligozat, LIMSI, France
Bernardo Magnini, FBK, HLT, Italy
Makoto Miwa, University of Manchester, UK
Claire Nedellec, MIG, INRA, France
Aurélie Névéol, LIMSI, France
Noaoaki Okazaki, Tohoku University, Japan
Christian Raymond, IRISA, France
Fabio Rinaldi, University of Zurich
Patrick Ruch, University of Geneva, Swiss
Benoit Sagot, ALPAGE, France
Satoshi Sekine, NYU, USA
Jian Su, A-STAR, Singapore
Junichi Tsujii, Microsoft Research Asia, China
Patrick Watrin, UCL, CENTAL, Belgique
Fabio Zanzotto, Tor Vergata, university of Rome, Italy
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| 7-5 | Special Issue of COMPUTER SPEECH AND LANGUAGE on Next Generation ParalinguisticsCall for Papers Special Issue of COMPUTER SPEECH AND LANGUAGE on Next Generation
Paralinguistics __________________________________
http://www.journals.elsevier.com/computer-speech-and-language/call-for-papers/next-generation-computational-paralinguistics/ Computational Paralinguistics recently reached a level of maturity allowing for their first real-life applications in interaction, coaching, media retrieval, robotics, surveillance, and manifold further
domains. In particular, an increasing level of realism is recently faced by coping with speaker
independent analysis of highly naturalistic data in narrow-bandwidth, noisy, or reverberated
conditions. At the same time, the richness of the range of speaker states and traits analysed
computationally is increasingly widening up. This includes in particular also the degree of
subjectivity faced with tasks such as perceived speaker personality, likability, or intelligibility,
to name a few. Both these aspects require additional experience on the interplay of states
and traits in speech, singing, and language. Further, with the integration in applications,
novel aspects arise such as efficiency, reliability, self-learning, mobility, multi-cultural and
multi-lingual aspects, handling groups of speaker or singers, standardisation, and user
experience with such systems. This Special Issue thus aims at shaping the Next Generation Computational Paralinguistics.
It will focus on technical issues for highly improved and reliable state and trait analysis in spoke
this topic. Original, previously unpublished submissions are encouraged within the following n,
sung, and written language and provide a forum for some of the very best experimental work
scope: +Analysis of States and Traits in Spoken, Sung, and Written Language +Subjectivity in Computational Paralinguistics (e.g., perceived states and traits) +Interdependence of States and Traits +Intelligibility of Language Varieties and Deviant Speech +Efficiency (low energy and memory consumption, fast adaptation, active learning, etc.) +Reliability (e.g., confidence measures, robustness against regulation and feigning, overlap) +Self-learning (unsupervised, partially supervised, reinforced, and deep learning) +Mobility (client/server distribution, package loss, coding artefacts, privacy preservation, etc.) +Multicultural and Multilingual Issues +Speaker / Singer Group Characterisation +Standardisation (output encoding, feature encoding, etc.) +Application (interaction, voice and writing coaching, retrieval, robotics, surveillance, etc.) +User Experience of Computational Paralinguistics Systems Important Dates __________________________________ Submission Deadline 1 April 2013 First Notification 1 July 2013 Final Version of Manuscripts 1 November 2013 Tentative Publication Date January 2014 Guest Editors __________________________________ Björn Schuller, Technische Universität München, Germany, schuller@IEEE.org Stefan Steidl, FAU, Germany, stefan.steidl@fau.de Anton Batliner, Technische Universität München, Germany, Anton.Batliner@lrz.uni-muenchen.de Alessandro Vinciarelli, University of Glasgow / IDIAP, UK, alessandro.vinciarelli@glasgow.ac.uk Felix Burkhardt, Deutsche Telekom AG, Germany, Felix.Burkhardt@telekom.de Rob van Son, University of Amsterdam / Netherlands Cancer Institute, NL , r.v.son@nki.nl Submission Procedure __________________________________ Prospective authors should follow the regular guidelines of the Computer Speech and Language Journal for electronic submission (http://ees.elsevier.com/csl). During submission
authors must select for this Special Issue (short name 'NextGen Paralinguistics').
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| 7-6 | CfP Special Issue of Neural Networks (Elsevier) on Affective and Cognitive Learning Systems for Big Social Data Analysis Special Issue of Neural Networks (Elsevier) on
Affective and Cognitive Learning Systems for Big Social Data Analysis
Guest Editors Amir Hussain*, University of Stirling, United Kingdom (ahu@cs.stir.ac.uk) Background and Motivation As the Web rapidly evolves, Web users are evolving with it. In an era of social connectedness, people are becoming more and more enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the Web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today's Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction. Existing approaches to opinion mininig mainly rely on parts of text in which sentiment is explicitly expressed, e.g., through polarity terms or affect words (and their co-occurrence frequencies). However, opinions and sentiments are often conveyed implicitly through latent semantics, which make purely syntactical approaches ineffective. In this light, this Special Issue focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply big data analysis tools and techniques for sentiment analysis. A key motivation for this Special Issue, in particular, is to explore the adoption of novel affective and cognitive learning systems to go beyond a mere word-level analysis of natural language text and provide novel concept-level tools and techniques that allow a more efficient passage from (unstructured) natural language to (structured) machine-processable data, in potentially any domain. Articles are thus invited in areas such as machine learning, weakly supervised learning, active learning, transfer learning, deep neural networks, novel neural and cognitive models, data mining, pattern recognition, knowledge-based systems, information retrieval, natural language processing, and big data computing. Topics include, but are not limited to: • Machine learning for big social data analysis The Special Issue also welcomes papers on specific application domains of big social data Timeframe Call for Papers out: April 2013 Composition and Review Procedures The Elsevier Neural Networks Special Issue on Affective and Cognitive Learning Systems for Big Social Data Analysis will consist of papers on novel methods and techniques that further develop and apply big data analysis tools and techniques in the context of opinion mining and sentiment analysis. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue's impact. All articles are expected to successfully negotiate the standard review procedures for Elsevier Neural Networks.
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Univ.-Prof. Dr.-Ing. habil. Björn W. Schuller
Head Institute for Sensor Systems University of Passau Passau / Germany
Head Machine Intelligence & Signal Processing Group Institute for Human-Machine Communication Technische Universität München Munich / Germany
CEO audEERING UG (haftungsbeschränkt) Gilching / Germany
Visiting Professor School of Computer Science and Technology Harbin Institute of Technology Harbin / P.R. China
Associate Institute for Information and Communication Technologies JOANNEUM RESEARCH Graz / Austria
Associate Centre Interfacultaire en Sciences Affectives Université de Genève Geneva / Switzerland
schuller@ieee.org http://www.schuller.it ___________________________________________
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| 7-7 | CFP: International Journal of Multimedia Information Retrieval -Special Issue on Cross-Media AnalysisCFP: International Journal of Multimedia Information Retrieval Special Issue on Cross-Media Analysis http://www.fortune.binghamton.edu/CFP_CMA_IJMIR2013.html Cross-media analysis is a new, emerging, and important research area in current multimedia research. Cross-media analysis exploits the data available on diverse sources of rich multimedia content simultaneously and synergistically. It is beneficial for many applications in data mining, causal inference, machine learning, multimedia, and public security. For more details, please see the special issue webpage above or contact one of the guest editors. Duedate for submissions: July 15th, 2013 Submissions Webpage: http://www.editorialmanager.com/mmir/default.asp Guest Editors: Zhongfei (Mark) Zhang SUNY Binghamton, USA zhongfei@cs.binghamton.edu Yueting Zhuang Zhejiang University, China yzhuang@cs.zju.edu.cn Ramesh Jain University of California, Irvine, USA jain@ics.uci.edu Jia-Yu (Tim) Pan Google, USA jypan@google.com
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