In this paper, the problem of disambiguating a target word for Polish is approached by searching for related words with known meaning. These relatives are used to build a training corpus from unannotated text. This technique is improved by proposing new rich sources of replacements that substitute the traditional requirement of monosemy with heuristics based on wordnet relations. The na "ive Bayesian classifier has been modified to account for an unknown distribution of senses. A corpus of 600 million web documents (594 billion tokens), gathered by the NEKST search engine allows us to assess the relationship between training set size and disambiguation accuracy. The classifier is evaluated using both a wordnet baseline and a corpus with 17,314 manually annotated occurrences of 54 ambiguous words.
There are a variety of approaches to solve the WSD problem, which can be grouped based upon how they use their data -- see reviews @cite_18 @cite_8 . In supervised solutions a large sense-tagged corpus is available for training. This approach has been applied to the the test set used in the current study, resulting in an accuracy value of 91.5 In the minimally supervised approach @cite_4 , a small set of initial training examples, obtained by a heuristic or hand-tagging, is used to label new occurrences. They in turn serve as a training set for next iteration, and so on. This bootstrapping procedure requires very little manual tagging but needs to be carefully implemented to avoid loosing accuracy in further steps.
@cite_18 @cite_4 @cite_8
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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. This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints---that words tend to have one sense per discourse and one sense per collocation---exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96 . The problem and process of identifying the meaning of a word as per its usage context is called word sense disambiguation (WSD). Although research in this field has been ongoing for the past forty years, a distinct change of techniques adopted can be observed over time. Two important parameters govern the direction in which WSD research progresses during any period. These are the underlying requirement of the kind of sense disambiguation, or the domain, and the robustness of available knowledge in the form of corpora or dictionaries. This paper surveys the progress of WSD over time and the important linguistic achievements that enabled this progress.