Unsupervised Part of Speech Tagging for Persian
نویسندگان
چکیده
منابع مشابه
Unsupervised Part of Speech Tagging for Persian
In this paper we present a rather novel unsupervised method for part of speech (below POS) disambiguation which has been applied to Persian. This method known as Iterative Improved Feedback (IIF) Model, which is a heuristic one, uses only a raw corpus of Persian as well as all possible tags for every word in that corpus as input. During the process of tagging, the algorithm passes through sever...
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Diierent approaches have been taken in order to solve the part-of-speech tagging problem. Several methods for unsupervised tagging have obtained good accuracies in practice. The approach taken by Brill Bri95] obtains results comparable to the best existing taggers. In this paper we explore the details of this unsupervised part-of-speech tagger and we present a comparison to the Xerox tagger, wh...
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One of the important actions in the processing of languages is part-of-speech tagging. Against of this importance, although numerous models have been presented in different languages but there is few works have been done in Persian language. In this paper, a part-of-speech tagging system on Persian corpus by using hidden Markov model is proposed. Achieving to this goal, the main aspects of Pers...
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There are numerous strategies for designing POS taggers for a specific language; rule-based, probabilistic, hybrid. We focus on unsupervised approaches, i.e. learning tagging probabilities from unlabeled text (Figure 2). This has the potential to speedily scale to any language as it does not require copious amounts of labeled text (supervised training data) or an exhaustive list of handcoded ru...
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Based on simple methods such as observing word and part of speech tag co-occurrence and clustering, we generate syntactic parses of sentences in an entirely unsupervised and self-inducing manner. The parser learns the structure of the language in question based on measuring ‘breaking points’ within sentences. The learning process is divided into two phases, learning and application of learned k...
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ژورنال
عنوان ژورنال: International Journal of Artificial Intelligence & Applications
سال: 2012
ISSN: 0976-2191
DOI: 10.5121/ijaia.2012.3204