Jacquemin, C.: Spotting and discovering terms through natural language processing (2001)
0.01
0.009479279 = product of:
0.028437834 = sum of:
0.028437834 = product of:
0.05687567 = sum of:
0.05687567 = weight(_text_:indexing in 119) [ClassicSimilarity], result of:
0.05687567 = score(doc=119,freq=4.0), product of:
0.19018644 = queryWeight, product of:
3.8278677 = idf(docFreq=2614, maxDocs=44218)
0.049684696 = queryNorm
0.29905218 = fieldWeight in 119, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.8278677 = idf(docFreq=2614, maxDocs=44218)
0.0390625 = fieldNorm(doc=119)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- In this book Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR). Jacquemin's novel tool is FASTR, a parser that normalizes terms and recognizes term variants. Since there are more meanings in a language than there are words, FASTR uses a metagrammar composed of shallow linguistic transformations that describe the morphological, syntactic, semantic, and pragmatic variations of words and terms. The acquired parsed terms can then be applied for precise retrieval and assembly of information. The use of a corpus-based unification grammar to define, recognize, and combine term variants from their base forms allows for intelligent information access to, or "linguistic data tuning" of, heterogeneous texts. FASTR can be used to do automatic controlled indexing, to carry out content-based Web searches through conceptually related alternative query formulations, to abstract scientific and technical extracts, and even to translate and collect terms from multilingual material. Jacquemin provides a comprehensive account of the method and implementation of this innovative retrieval technique for text processing.
