Special Issue "Advances in Natural Language Processing"

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A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 March 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Horacio Saggion

TALN Group, Department of Information and Communication Technologies, Pompeu Fabra University, C/Tànger, 122-134, 4th floor, 08018 Barcelona, Spain
Website | E-Mail
Phone: +34 93 542 1119

Special Issue Information

Dear Colleagues,

Texts are one of the most important records of human expertise, therefore being of paramount importance for mining both general and specific knowledge. Over the past few years the role of Natural Language Processing (NLP) for mining information from textual sources has gained relevance due to the now massive availability of textual information on-line and the need to access, distill, and organize the contents held in this wealth of unstructured information. This special issue of Informatics aims to bring together articles that report advances in Natural Language Processing, both experimental as well practical applications, related to the exploitation and distillation of textual material for information access and knowledge creation. We are therefore calling for contributions in the areas of Automatic Text Summarization, Adaptable Information Extraction and Knowledge Population, Knowledge Induction from Text, Text Simplification, Text Entailment and Learning by Reading, and Natural Language Processing for the Social Media.

Prof. Dr. Horacio Saggion
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Published Papers (2 papers)

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Research

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Open AccessArticle Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining
Informatics 2014, 1(1), 32-51; doi:10.3390/informatics1010032
Received: 24 September 2013 / Revised: 11 November 2013 / Accepted: 21 November 2013 / Published: 28 November 2013
Cited by 1 | PDF Full-text (252 KB) | HTML Full-text | XML Full-text
Abstract
Numerous initiatives have allowed users to share knowledge or opinions using collaborative platforms. In most cases, the users provide a textual description of their knowledge, following very limited or no constraints. Here, we tackle the classification of documents written in such an environment.
[...] Read more.
Numerous initiatives have allowed users to share knowledge or opinions using collaborative platforms. In most cases, the users provide a textual description of their knowledge, following very limited or no constraints. Here, we tackle the classification of documents written in such an environment. As a use case, our study is made in the context of text mining evaluation campaign material, related to the classification of cooking recipes tagged by users from a collaborative website. This context makes some of the corpus specificities difficult to model for machine-learning-based systems and keyword or lexical-based systems. In particular, different authors might have different opinions on how to classify a given document. The systems presented hereafter were submitted to the D´Efi Fouille de Textes 2013 evaluation campaign, where they obtained the best overall results, ranking first on task 1 and second on task 2. In this paper, we explain our approach for building relevant and effective systems dealing with such a corpus. Full article
(This article belongs to the Special Issue Advances in Natural Language Processing)

Review

Jump to: Research

Open AccessReview On Collocations and Their Interaction with Parsing and Translation
Informatics 2014, 1(1), 11-31; doi:10.3390/informatics1010011
Received: 1 September 2013 / Revised: 3 October 2013 / Accepted: 16 October 2013 / Published: 25 October 2013
PDF Full-text (237 KB) | HTML Full-text | XML Full-text
Abstract
We address the problem of automatically processing collocations—a subclass of multi-word expressions characterized by a high degree of morphosyntactic flexibility—in the context of two major applications, namely, syntactic parsing and machine translation. We show that parsing and collocation identification are processes that are
[...] Read more.
We address the problem of automatically processing collocations—a subclass of multi-word expressions characterized by a high degree of morphosyntactic flexibility—in the context of two major applications, namely, syntactic parsing and machine translation. We show that parsing and collocation identification are processes that are interrelated and that benefit from each other, inasmuch as syntactic information is crucial for acquiring collocations from corpora and, vice versa, collocational information can be used to improve parsing performance. Similarly, we focus on the interrelation between collocations and machine translation, highlighting the use of translation information for multilingual collocation identification, as well as the use of collocational knowledge for improving translation. We give a panorama of the existing relevant work, and we parallel the literature surveys with our own experiments involving a symbolic parser and a rule-based translation system. The results show a significant improvement over approaches in which the corresponding tasks are decoupled. Full article
(This article belongs to the Special Issue Advances in Natural Language Processing)

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