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- 826 creator ruslan-mitkov.
- 826 creator todor-arnaudov.
- 826 type InProceedings.
- 826 label "Smarty - Extendable Framework for Bilingual and Multilingual Comprehension Assistants".
- 826 sameAs 826.
- 826 abstract "This paper discusses a framework for development of bilingual and multilingual comprehension assistants and presents a prototype implementation of an English-Bulgarian comprehension assistant. The framework is based on the application of advanced graphical user interface techniques, WordNet and compatible lexical databases as well as a series of NLP preprocessing tasks, including POS-tagging, lemmatisation, multiword expressions recognition and word sense disambiguation. The aim of this framework is to speed up the process of dictionary look-up, to offer enhanced look-up functionalities and to perform a context-sensitive narrowing-down of the set of translation alternatives proposed to the user.".
- 826 hasAuthorList authorList.
- 826 hasTopic Linguistics.
- 826 isPartOf proceedings.
- 826 keyword "Lexicon, lexical database".
- 826 keyword "Multilinguality".
- 826 title "Smarty - Extendable Framework for Bilingual and Multilingual Comprehension Assistants".
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- 827 creator dan-parvaz.
- 827 creator karine-megerdoomian.
- 827 type InProceedings.
- 827 label "Low-Density Language Bootstrapping: the Case of Tajiki Persian".
- 827 sameAs 827.
- 827 abstract "Low-density languages raise difficulties for standard approaches to natural language processing that depend on large online corpora. Using Persian as a case study, we propose a novel method for bootstrapping MT capability for a low-density language in the case where it relates to a higher density variant. Tajiki Persian is a low-density language that uses the Cyrillic alphabet, while Iranian Persian (Farsi) is written in an extended version of the Arabic script and has many computational resources available. Despite the orthographic differences, the two languages have literary written forms that are almost identical. The paper describes the development of a comprehensive finite-state transducer that converts Tajik text to Farsi script and runs the resulting transliterated document through an existing Persian-to-English MT system. Due to divergences that arise in mapping the two writing systems and phonological and lexical distinctions, the system uses contextual cues (such as the position of a phoneme in a word) as well as available Farsi resources (such as a morphological analyzer to deal with differences in the affixal structures and a lexicon to disambiguate the analyses) to control the potential combinatorial explosion. The results point to a valuable strategy for the rapid prototyping of MT packages for languages of similar uneven density.".
- 827 hasAuthorList authorList.
- 827 hasTopic Linguistics.
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- 827 keyword "Endangered languages".
- 827 keyword "Machine Translation, SpeechToSpeech Translation".
- 827 keyword "Multilinguality".
- 827 title "Low-Density Language Bootstrapping: the Case of Tajiki Persian".
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- 828 creator ana-gonzalez-ledesma.
- 828 creator doaa-samy.
- 828 type InProceedings.
- 828 label "Pragmatic Annotation of Discourse Markers in a Multilingual Parallel Corpus (Arabic- Spanish-English)".
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- 828 abstract "Discourse structure and coherence relations are one of the main inferential challenges addressed by computational pragmatics. The present study focuses on discourse markers as key elements in guiding the inferences of the statements in natural language. Through a rule-based approach for the automatic identification, classification and annotation of the discourse markers in a multilingual parallel corpus (Arabic-Spanish-English), this research provides a valuable resource for the community. Two main aspects define the novelty of the present study. First, it offers a multilingual computational processing of discourse markers, grounded on a theoritical framework and implemented in a XML tagging scheme. The XML scheme represents a set of pragmatic and grammatical attributes, considered as basic features for the different kinds of discourse markers. Besides, the scheme provides a typology of discourse markers based on their discursive functions including hypothesis, co-argumentation, cause, consequence, concession, generalization, topicalization, reformulation, enumeration, synthesis, etc. Second, Arabic language is addressed from a computational pragmatic perspective where the identification, classification and annotation processes are carried out using the information provided from the tagging of Spanish discourse markers and the alignments.".
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- 828 keyword "Corpus (creation, annotation, etc.)".
- 828 keyword "Multilinguality".
- 828 keyword "Tagging".
- 828 title "Pragmatic Annotation of Discourse Markers in a Multilingual Parallel Corpus (Arabic- Spanish-English)".
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- 83 creator ben-allison.
- 83 creator louise-guthrie.
- 83 creator nasser-abouzakhar.
- 83 type InProceedings.
- 83 label "Unsupervised Learning-based Anomalous Arabic Text Detection".
- 83 sameAs 83.
- 83 abstract "The growing dependence of modern society on the Web as a vital source of information and communication has become inevitable. However, the Web has become an ideal channel for various terrorist organisations to publish their misleading information and send unintelligible messages to communicate with their clients as well. The increase in the number of published anomalous misleading information on the Web has led to an increase in security threats. The existing Web security mechanisms and protocols are not appropriately designed to deal with such recently developed problems. Developing technology to detect anomalous textual information has become one of the major challenges within the NLP community. This paper introduces the problem of anomalous text detection by automatically extracting linguistic features from documents and evaluating those features for patterns of suspicious and/or inconsistent information in Arabic documents. In order to achieve that, we defined specific linguistic features that characterise various Arabic writing styles. Also, the paper introduces the main challenges in Arabic processing and describes the proposed unsupervised learning model for detecting anomalous Arabic textual information.".
- 83 hasAuthorList authorList.
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- 83 keyword "Acquisition, Machine Learning".
- 83 keyword "Document Classification, Text categorisation".
- 83 keyword "Information Extraction, Information Retrieval".
- 83 title "Unsupervised Learning-based Anomalous Arabic Text Detection".
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