Part-of-Speech Tagging Guidelines for the Penn Treebank Project
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The Part-Of-Speech Tagging Guidelines for the Penn Chinese Treebank (3.0)
This document describes the Part-of-Speech (POS) tagging guidelines for the Penn Chinese Treebank Project. The goal of the project is the creation of a 100-thousand-word corpus of Mandarin Chinese text with syntactic bracketing. The Chinese Treebank has been released via the Linguistic Data Consortium (LDC) and is available to the public. The POS tagging guidelines have been revised several tim...
متن کاملبرچسبگذاری ادات سخن زبان فارسی با استفاده از مدل شبکۀ فازی
Part of speech tagging (POS tagging) is an ongoing research in natural language processing (NLP) applications. The process of classifying words into their parts of speech and labeling them accordingly is known as part-of-speech tagging, POS-tagging, or simply tagging. Parts of speech are also known as word classes or lexical categories. The purpose of POS tagging is determining the grammatical ...
متن کاملThe Penn Treebank: an Overview
The Penn Treebank, in its eight years of operation (1989-1996), produced approximately 7 million words of part-of-speech tagged text, 3 million words of skeletally parsed text, over 2 million words of text parsed for predicateargument structure, and 1.6 million words of transcribed spoken text annotated for speech disfluencies. This paper describes the design of the three annotation schemes use...
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With growing interest in Korean language processing, numerous natural languages processing (NLP) tools for Korean, such as part-of-speech (POs) taggers, morphological analyzers , parsers, have been developed. This progress was possible through the availability of large-scale raw text corpora and POS tagged corpora (ETRI, 1999; Yoon and Choi, 1999a; Yoon and Choi, 1999b). However, no large-scale...
متن کاملBUILDING AN EFFICIENT, SCALABLE, AND TRAINABLE PROBABILITY-AND-RULE- BASED PART-OF-SPEECH TAGGER OF HIGH ACCURACY by
This project is aimed to build an efficient, scalable, portable, and trainable part-of-speech tagger. Using 98% of Penn Treebank-3 as the training data, it builds a raw tagger, using Bayes’ theorem, a hidden Markov model, and the Viterbi algorithm. After that, a reinforcement machine learning algorithm and contextual transformation rules were applied to increase the tagger’s accuracy. The tagge...
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