Search (4 results, page 1 of 1)

0.018442601 = product of:
  0.092213005 = sum of:
    0.092213005 = weight(_text_:index in 4224) [ClassicSimilarity], result of:
      0.092213005 = score(doc=4224,freq=4.0), product of:
        0.2250935 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.051511593 = queryNorm
        0.40966535 = fieldWeight in 4224, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.046875 = fieldNorm(doc=4224)
  0.2 = coord(1/5)

Abstract

Many operational IR indexes are non-normalized, i.e. no lemmatization or stemming techniques, etc. have been employed in indexing. This poses a challenge for dictionary-based cross-language retrieval (CLIR), because translations are mostly lemmas. In this study, we face the challenge of dictionary-based CLIR in a non-normalized index. We test two optional approaches: FCG (Frequent Case Generation) and s-gramming. The idea of FCG is to automatically generate the most frequent inflected forms for a given lemma. FCG has been tested in monolingual retrieval and has been shown to be a good method for inflected retrieval, especially for highly inflected languages. S-gramming is an approximate string matching technique (an extension of n-gramming). The language pairs in our tests were English-Finnish, English-Swedish, Swedish-Finnish and Finnish-Swedish. Both our approaches performed quite well, but the results varied depending on the language pair. S-gramming and FCG performed quite equally in all the other language pairs except Finnish-Swedish, where s-gramming outperformed FCG.

0.015368836 = product of:
  0.07684418 = sum of:
    0.07684418 = weight(_text_:index in 4215) [ClassicSimilarity], result of:
      0.07684418 = score(doc=4215,freq=4.0), product of:
        0.2250935 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.051511593 = queryNorm
        0.3413878 = fieldWeight in 4215, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4215)
  0.2 = coord(1/5)

Abstract

For historical and cultural reasons, English phrases, especially proper nouns and new words, frequently appear in Web pages written primarily in East Asian languages such as Chinese, Korean, and Japanese. Although such English terms and their equivalences in these East Asian languages refer to the same concept, they are often erroneously treated as independent index units in traditional Information Retrieval (IR). This paper describes the degree to which the problem arises in IR and proposes a novel technique to solve it. Our method first extracts English terms from native Web documents in an East Asian language, and then unifies the extracted terms and their equivalences in the native language as one index unit. For Cross-Language Information Retrieval (CLIR), one of the major hindrances to achieving retrieval performance at the level of Mono-Lingual Information Retrieval (MLIR) is the translation of terms in search queries which can not be found in a bilingual dictionary. The Web mining approach proposed in this paper for concept unification of terms in different languages can also be applied to solve this well-known challenge in CLIR. Experimental results based on NTCIR and KT-Set test collections show that the high translation precision of our approach greatly improves performance of both Mono-Lingual and Cross-Language Information Retrieval.

0.010867408 = product of:
  0.054337036 = sum of:
    0.054337036 = weight(_text_:index in 2372) [ClassicSimilarity], result of:
      0.054337036 = score(doc=2372,freq=2.0), product of:
        0.2250935 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.051511593 = queryNorm
        0.24139762 = fieldWeight in 2372, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2372)
  0.2 = coord(1/5)

Abstract

Out of vocabulary words, mostly proper nouns and technical terms, are one main source of performance degradation in Cross Language Information Retrieval (CLIR) systems. Those are words not found in the dictionary. Bilingual dictionaries in general do not cover most proper nouns, which are usually primary keys in the query. As they are spelling variants of each other in most languages, using an approximate string matching technique against the target database index is the common approach taken to find the target language correspondents of the original query key. N-gram technique proved to be the most effective among other string matching techniques. The issue arises when the languages dealt with have different alphabets. Transliteration is then applied based on phonetic similarities between the languages involved. In this study, both transliteration and the n-gram technique are combined to generate possible transliterations in an English-Arabic CLIR system. We refer to this technique as Transliteration N-Gram (TNG). We further enhance TNG by applying Part Of Speech disambiguation on the set of transliterations so that words with a similar spelling, but a different meaning, are excluded. Experimental results show that TNG gives promising results, and enhanced TNG further improves performance.

0.008374932 = product of:
  0.04187466 = sum of:
    0.04187466 = weight(_text_:22 in 4436) [ClassicSimilarity], result of:
      0.04187466 = score(doc=4436,freq=2.0), product of:
        0.18038483 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.051511593 = queryNorm
        0.23214069 = fieldWeight in 4436, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.046875 = fieldNorm(doc=4436)
  0.2 = coord(1/5)

Date

16. 2.2000 14:22:39