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.
Preparing a corpus for finding relatives poses a challenge as well. It should contain a lot of text, as many monosemous words are scarce. Some researchers use snippets retrieved from search engines, i.e. AltaVista @cite_13 or Google @cite_22 . One can also extend a search query by including the context of the disambiguated word @cite_10 , but it requires using as many queries as test cases.
@cite_10 @cite_13 @cite_22
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The current situation for Word Sense Disambiguation (WSD) is somewhat stuck due to lack of training data. We present in this paper a novel disambiguation algorithm that improves previous systems based on acquisition of examples by incorporating local context information. With a basic configuration, our method is able to obtain state-of-the-art performance. We complemented this work by evaluating other well-known methods in the same dataset, and analysing the comparative results per word. We observed that each algorithm performed better for different types of words, and each of them failed for some particular words. We proposed then a simple unsupervised voting scheme that improved significantly over single systems, achieving the best unsupervised performance on both the Senseval 2 and Senseval 3 lexical sample datasets. The unavailability of very large corpora with semantically disambiguated words is a major limitation in text processing research. For example, statistical methods for word sense disambiguation of free text are known to achieve high accuracy results when large corpora are available to develop context rules, to train and test them.This paper presents a novel approach to automatically generate arbitrarily large corpora for word senses. The method is based on (1) the information provided in WordNet, used to formulate queries consisting of synonyms or definitions of word senses, and (2) the information gathered from Internet using existing search engines. The method was tested on 120 word senses and a precision of 91 was observed. This paper explores the large-scale acquisition of sense-tagged examples for Word Sense Disambiguation (WSD). We have applied the “WordNet monosemous relatives” method to construct automatically a web corpus that we have used to train disambiguation systems. The corpus-building process has highlighted important factors, such as the distribution of senses (bias). The corpus has been used to train WSD algorithms that include supervised methods (combining automatic and manuallytagged examples), minimally supervised (requiring sense bias information from hand-tagged corpora), and fully unsupervised. These methods were tested on the Senseval-2 lexical sample test set, and compared successfully to other systems with minimum or no supervision.