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Some linguistic methods of improving the quality of document retrieval on the internet
(Inderscience Publishers, 2005-01) Gelbukh, Alexander; Sidorov, Grigori; Ledo Mezquita, Yoel; Instituto Politécnico Nacional y Chung-Ang University; https://ror.org/03ayjn504
One of the problems of e-Business is to find relevant documents for making correct decisions. The main problem of the Internet is the huge amount of documents that makes it difficult to find the relevant ones, hence the importance of the methods allowing for improving the quality of document retrieval. We discuss some linguistic problems of document retrieval on the Internet related to the following natural language phenomena: (1) morphological processes: e.g., takes, took, taken are grammar forms of take, (2) polysemy and homonymy: most words have several senses, e.g., bank is a financial institution, shore, bench, etc., (3) non-linearity of syntactic relations: in case of a query that contains word combinations, the words forming a word combination can be separated by other words in the documents. Some linguistic-based methods and strategies related to the discussed problems are proposed that improve the quality of document retrieval or show the necessity of application of linguistic methods.
On similarity of word senses in explanatory dictionaries
(Bahri Publications, 2003-01) Gelbukh, Alexander; Sidorov, Grigori; Ledo Mezquita, Yoel; Instituto Politécnico Nacional; https://ror.org/059sp8j34; https://ror.org/03ayjn504
Quality machine translation (MT) as well as of some other applications (such as information retrieval, IR) require word sense disambiguation (WSD) in the source text. However, WSD is only possible if the word senses specified in the dictionaries are really different and clearly distinguishable. We investigate the semantic closeness of different senses of the same word in a Spanish explanatory dictionary. We define the closeness between two senses as the relative number of equal or synonymous words in their definitions. We show that a considerable part of dictionary definitions (ca. 90%) are different enough to be distinguished in MT and IR. On the other hand, a considerable number of definitions (ca. 10%) are too similar to be reliably distinguished. These results suggest that MT and IR can take advantage of WSD algorithms, but for this, the similar senses reflecting too subtle meaning nuances should be clustered together to form coarser but easier distinguishable senses. The proposed method for detecting too similar senses can be incorporated into the lexicographer’s workbench to be used in development and improvement of dictionaries.

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