Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.

A context-based spelling error is a spelling or typing error that turns an intended word into another word of the language. For example, the intended word "sight" might become the word "site." A spell checker cannot identify such an error. In the English language---the case of interest here---a syntax checker may also fail to catch such an error since, among other reasons, the parts-of-speech of an erroneous word may permit an acceptable parsing. This chapter presents an effective method called Ltest for identifying the majority of context-based spelling errors. Ltest learns from

This paper presents a new approach for word prediction problem. Word prediction is a natural language processing problem that tries to predict the correct word in a given context. Word completion utilities, writing aids, and language translation are among the most common applications of word prediction. In this paper, we describe a new method to predict the correct word given its context. A data mining tool is used as a classification mean to predict the correct word in the given context. The method has been implemented; the testing results are promising. The approach requires a very small training text size compared with similar methods to produce an accuracy that approaches 93% correct predictions.

The development of automatic natural language understanding systems remains an elusive goal. Given the highly ambiguous nature of the syntax and semantics of natural language, it is not possible to develop rule--based approaches to understanding even very limited domains of text. The difficulty in specifying a complete set of rules and their exceptions has led to the rise of probabilistic approaches where models of natural language are learned from large corpora of text. However, this has proven a challenge since natural language data is both sparse and skewed and the space of possible models is huge. In this paper we discuss several search techniques used in learning the structure of probabilistic models of word sense disambiguation. We present an experimental comparison of backward and forward sequential searches as well as a model averaging approach to the problem of resolving the meaning of ambiguous words in text. Introduction The difficulty in specifying complete and consistent...

VERSION 0.3 This document is intended for computer scientists who would like to try out a Markov Chain Monte Carlo (MCMC) technique, particularly in order to do inference with Bayesian models on problems related to text processing. We try to keep theory to the absolute minimum needed, and we work through the details much more explicitly than you usually see even in “introductory ” explanations. That means we’ve attempted to be ridiculously explicit in our exposition and notation. After providing the reasons and reasoning behind Gibbs sampling (and at least nodding our heads in the direction of theory), we work through two applications in detail. The first is the derivation of a Gibbs sampler for Naive Bayes models, which illustrates a simple case where the math works out very cleanly and it’s possible to “integrate out ” the model’s continuous parameters to build a more efficient algorithm. The second application derives the Gibbs sampler for a model that is similar to Naive Bayes, but which adds an additional latent variable. Having gone through the two examples, we discuss some practical implementation issues. We conclude with some pointers to literature that we’ve found to be somewhat more friendly to uninitiated readers. 1

Abstract: Word prediction is an important NLP problem in which we want to predict the correct word in a given Word completion utilities, predictive text entry systems, writing aids, and language translation are some of common word prediction applications. This paper presents a new word prediction approach based on context features and machine learning. The proposed method casts the problem as a learning-classification task by training word predictors with highly discriminating features selected by various feature selection techniques. The contribution of this work lies in the new way of presenting this problem, and the unique combination of a top performer in machine learning, svm, with various feature selection techniques MI, X 2, and more. The method is implemented and evaluated using several datasets. The experimental results show clearly that the method is effective in predicting the correct words by utilizing small contexts. The system achieved impressive results, compared with similar work; the accuracy in some experiments approaches 91 % correct predictions.

Define an inadvertent semantic error to be a spelling or typing error that turns an intended word into another word of the language. For example, the intended word "sight" might become the word "site." A spell checker cannot identify such an error. In the English language---the case of interest here---a syntax checker may also fail to catch such an error since the parts-of-speech of an erroneous word may permit an acceptable parsing. In addition, error detection by a syntax checker likely is difficult if the text contains many special terms, symbols, formulas, or conventions whose syntactic contributions cannot be established without a complete understanding of the text. For such texts, as well as for texts that do not involve such complicating aspects, this paper presents an effective technique for identifying the majority of inadvertent semantic errors. The method requires a prior text that does not contain inadvertent semantic errors and that, in terms of word usage and style, is r...