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Lingvisticæ Investigationes

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ISSN 0378-4169
E-ISSN 1569-9927

<p><em>Lingvisticæ Investigationes</em> publishes original articles dealing with the lexicon, grammar, phonology and semantics. It focuses on studies that are formalized to the point where they can be integrated into text analysis software, and on studies which describe resources such as grammars and electronic dictionaries constructed on a linguistic basis. The journal also publishes bibliographies, summaries of theses, reports, squibs and reviews. Contributions are in English and French. French-speaking authors are free to submit in French or in English.</p><p>The journal has an accompanying book series entitled <em>Lingvisticæ Investigationes Supplementa</em>.</p>

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  • A survey of named entity recognition and classification
    • Authors: David Nadeau, and Satoshi Sekine
    • Source: Lingvisticæ Investigationes, Volume 30, Issue 1, 2007, pages: 3 –26
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    • This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress.
  • Whether We Agree or Not: A Comparative Syntax of English and Japanese
  • Appraisal of Opinion Expressions in Discourse
    • Authors: Nicholas Asher, Farah Benamara, and Yvette Yannick Mathieu
    • Source: Lingvisticæ Investigationes, Volume 32, Issue 2, 2009, pages: 279 –292
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    • We present an analysis of opinion in texts based on a detailed semantic analysis of a wide class of expressions. We propose a new annotation schema for a deep contextual opinion analysis using discourse relations. We analyze the distribution of our categories in three different types of online corpora, movie reviews, Letters to the Editor and news reports, in English and French.
  • What Do Conversational Maxims Explain?
  • Named Entity Recognition and transliteration in Bengali
    • Authors: Asif Ekbal, Sudip Kumar Naskar, and Sivaji Bandyopadhyay
    • Source: Lingvisticæ Investigationes, Volume 30, Issue 1, 2007, pages: 95 –114
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    • The paper reports about the development of a Named Entity Recognition (NER) system in Bengali using a tagged Bengali news corpus and the subsequent transliteration of the recognized Bengali Named Entities (NEs) into English. Three different models of the NER have been developed. A semi-supervised learning method has been adopted to develop the first two models, one without linguistic features (Model A) and the other with linguistic features (Model B). The third one (Model C) is based on statistical Hidden Markov Model. A modified joint-source channel model has been used along with a number of alternatives to generate the English transliterations of Bengali NEs and vice-versa. The transliteration models learn the mappings from the bilingual training sets optionally guided by linguistic knowledge in the form of conjuncts and diphthongs in Bengali and their representations in English. The NER system has demonstrated the highest average Recall, Precision and F-Score values of 89.62%, 78.67% and 83.79% respectively in Model C. Evaluation of the proposed transliteration models demonstrated that the modified joint source-channel model performs best in terms of evaluation metrics for person and location names for both Bengali to English (B2E) transliteration and English to Bengali transliteration (E2B). The use of the linguistic knowledge during training of the transliteration models improves performance.
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