-
Jacquemin, C.: Spotting and discovering terms through natural language processing (2001)
0.06
0.059540637 = product of:
0.11908127 = sum of:
0.0604585 = weight(_text_:data in 119) [ClassicSimilarity], result of:
0.0604585 = score(doc=119,freq=10.0), product of:
0.15478487 = queryWeight, product of:
3.1620505 = idf(docFreq=5088, maxDocs=44218)
0.04895079 = queryNorm
0.39059696 = fieldWeight in 119, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
3.1620505 = idf(docFreq=5088, maxDocs=44218)
0.0390625 = fieldNorm(doc=119)
0.058622777 = product of:
0.117245555 = sum of:
0.117245555 = weight(_text_:processing in 119) [ClassicSimilarity], result of:
0.117245555 = score(doc=119,freq=14.0), product of:
0.19816001 = queryWeight, product of:
4.048147 = idf(docFreq=2097, maxDocs=44218)
0.04895079 = queryNorm
0.5916711 = fieldWeight in 119, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
4.048147 = idf(docFreq=2097, maxDocs=44218)
0.0390625 = fieldNorm(doc=119)
0.5 = coord(1/2)
0.5 = coord(2/4)
- 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.
- LCSH
- Language and languages / Variation / Data processing
Terms and phrases / Data processing
- Subject
- Language and languages / Variation / Data processing
Terms and phrases / Data processing
-
Jurafsky, D.; Martin, J.H.: Speech and language processing : ani ntroduction to natural language processing, computational linguistics and speech recognition (2009)
0.02
0.015667595 = product of:
0.06267038 = sum of:
0.06267038 = product of:
0.12534076 = sum of:
0.12534076 = weight(_text_:processing in 1081) [ClassicSimilarity], result of:
0.12534076 = score(doc=1081,freq=16.0), product of:
0.19816001 = queryWeight, product of:
4.048147 = idf(docFreq=2097, maxDocs=44218)
0.04895079 = queryNorm
0.632523 = fieldWeight in 1081, product of:
4.0 = tf(freq=16.0), with freq of:
16.0 = termFreq=16.0
4.048147 = idf(docFreq=2097, maxDocs=44218)
0.0390625 = fieldNorm(doc=1081)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology at all levels and with all modern technologies this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material.
-
Hutchins, W.J.; Somers, H.L.: ¬An introduction to machine translation (1992)
0.01
0.0055393316 = product of:
0.022157326 = sum of:
0.022157326 = product of:
0.044314653 = sum of:
0.044314653 = weight(_text_:processing in 4512) [ClassicSimilarity], result of:
0.044314653 = score(doc=4512,freq=2.0), product of:
0.19816001 = queryWeight, product of:
4.048147 = idf(docFreq=2097, maxDocs=44218)
0.04895079 = queryNorm
0.22363065 = fieldWeight in 4512, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.048147 = idf(docFreq=2097, maxDocs=44218)
0.0390625 = fieldNorm(doc=4512)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- The translation of foreign language texts by computers was one of the first tasks that the pioneers of Computing and Artificial Intelligence set themselves. Machine translation is again becoming an importantfield of research and development as the need for translations of technical and commercial documentation is growing well beyond the capacity of the translation profession.This is the first textbook of machine translation, providing a full course on both general machine translation systems characteristics and the computational linguistic foundations of the field. The book assumes no previous knowledge of machine translation and provides the basic background information to the linguistic and computational linguistics, artificial intelligence, natural language processing and information science.