Conference on Applied Natural Language Processing 

About: Conference on Applied Natural Language Processing is an academic conference. The conference publishes majorly in the area(s): Natural language & Parsing. Over the lifetime, 273 publications have been published by the conference receiving 16641 citations.

A Simple Rule-Based Part of Speech Tagger

Abstract: Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule-based methods. In this paper, we present a simple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy comparable to stochastic taggers. The rule-based tagger has many advantages over these taggers, including: a vast reduction in stored information required, the perspicuity of a small set of meaningful rules, ease of finding and implementing improvements to the tagger, and better portability from one tag set, corpus genre or language to another. Perhaps the biggest contribution of this work is in demonstrating that the stochastic method is not the only viable method for part of speech tagging. The fact that a simple rule-based tagger that automatically learns its rules can perform so well should offer encouragement for researchers to further explore rule-based tagging, searching for a better and more expressive set of rule templates and other variations on the simple but effective theme described below.

TnT -- A Statistical Part-of-Speech Tagger

Abstract: Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework A recent comparison has even shown that TnT performs significantly better for the tested corpora We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words Furthermore, we present evaluations on two corpora

A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text

Abstract: A program that tags each word in an input sentence with the most likely part of speech has been written. The program uses a linear-time dynamic programming algorithm to find an assignment of parts of speech to words that optimizes the product of (a) lexical probabilities (probability of observing part of speech i given word i) and (b) contextual probabilities (probability of observing part of speech i given n following parts of speech). Program performance is encouraging; a 400-word sample is presented and is judged to be 99.5% correct. >

GATE - a General Architecture for Text Engineering

Abstract: Much progress has been made in the provision of reusable data resources for Natural Language Engineering, such as grammars, lexicons, thesauruscs. Although a number of projects have addressed the provision of reusable algorithmic resources (or 'tools'), takeup of these resources has been relatively slow. This paper describes GATE, a General Architecture for Text Engineering, which is a freely-available system designed to help alleviate the problem.

A Practical Part-of-Speech Tagger

Abstract: We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies and optimizations which result in high-speed operation. Three applications for tagging are described: phrase recognition; word sense disambiguation; and grammatical function assignment.