Search (33 results, page 1 of 2)

  • × year_i:[2000 TO 2010}
  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.07 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Ahmed, F.; Nürnberger, A.: Evaluation of n-gram conflation approaches for Arabic text retrieval (2009) 0.03 
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    Abstract
    In this paper we present a language-independent approach for conflation that does not depend on predefined rules or prior knowledge of the target language. The proposed unsupervised method is based on an enhancement of the pure n-gram model that can group related words based on various string-similarity measures, while restricting the search to specific locations of the target word by taking into account the order of n-grams. We show that the method is effective to achieve high score similarities for all word-form variations and reduces the ambiguity, i.e., obtains a higher precision and recall, compared to pure n-gram-based approaches for English, Portuguese, and Arabic. The proposed method is especially suited for conflation approaches in Arabic, since Arabic is a highly inflectional language. Therefore, we present in addition an adaptive user interface for Arabic text retrieval called araSearch. araSearch serves as a metasearch interface to existing search engines. The system is able to extend a query using the proposed conflation approach such that additional results for relevant subwords can be found automatically.
    Object
    n-grams
  3. Figuerola, C.G.; Gomez, R.; Lopez de San Roman, E.: Stemming and n-grams in Spanish : an evaluation of their impact in information retrieval (2000) 0.03 
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  4. Bellaachia, A.; Amor-Tijani, G.: Proper nouns in English-Arabic cross language information retrieval (2008) 0.02 
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    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.
  5. Kummer, N.: Indexierungstechniken für das japanische Retrieval (2006) 0.02 
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  6. Koppel, M.; Akiva, N.; Dagan, I.: Feature instability as a criterion for selecting potential style markers (2006) 0.02 
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  7. Bubenhofer, N.: Einführung in die Korpuslinguistik : Praktische Grundlagen und Werkzeuge (2006) 0.02 
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  8. Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.02 
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    Date
    1. 3.2013 14:56:22
  9. Monnerjahn, P.: Vorsprung ohne Technik : Übersetzen: Computer und Qualität (2000) 0.02 
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    Source
    c't. 2000, H.22, S.230-231
  10. Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004) 0.02 
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    Abstract
    This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.
  11. Melucci, M.; Orio, N.: Design, implementation, and evaluation of a methodology for automatic stemmer generation (2007) 0.02 
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  12. Bacchin, M.; Ferro, N.; Melucci, M.: ¬A probabilistic model for stemmer generation (2005) 0.02 
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  13. WordHoard: finding multiword units (20??) 0.02 
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    Abstract
    WordHoard defines a multiword unit as a special type of collocate in which the component words comprise a meaningful phrase. For example, "Knight of the Round Table" is a meaningful multiword unit or phrase. WordHoard uses the notion of a pseudo-bigram to generalize the computation of bigram (two word) statistical measures to phrases (n-grams) longer than two words, and to allow comparisons of these measures for phrases with different word counts. WordHoard applies the localmaxs algorithm of Silva et al. to the pseudo-bigrams to identify potential compositional phrases that "stand out" in a text. WordHoard can also filter two and three word phrases using the word class filters suggested by Justeson and Katz.
  14. Kuhlmann, U.; Monnerjahn, P.: Sprache auf Knopfdruck : Sieben automatische Übersetzungsprogramme im Test (2000) 0.02 
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    Source
    c't. 2000, H.22, S.220-229
  15. Beitzel, S.M.; Jensen, E.C.; Chowdhury, A.; Grossman, D.; Frieder, O; Goharian, N.: Fusion of effective retrieval strategies in the same information retrieval system (2004) 0.01 
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  16. Jensen, N.: Evaluierung von mehrsprachigem Web-Retrieval : Experimente mit dem EuroGOV-Korpus im Rahmen des Cross Language Evaluation Forum (CLEF) (2006) 0.01 
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  17. Argamon, S.; Whitelaw, C.; Chase, P.; Hota, S.R.; Garg, N.; Levitan, S.: Stylistic text classification using functional lexical features (2007) 0.01 
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  18. Airio, E.; Kettunen, K.: Does dictionary based bilingual retrieval work in a non-normalized index? (2009) 0.01 
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    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.
  19. ¬The semantics of relationships : an interdisciplinary perspective (2002) 0.01 
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    Content
    Enthält die Beiträge: Pt.1: Types of relationships: CRUDE, D.A.: Hyponymy and its varieties; FELLBAUM, C.: On the semantics of troponymy; PRIBBENOW, S.: Meronymic relationships: from classical mereology to complex part-whole relations; KHOO, C. u.a.: The many facets of cause-effect relation - Pt.2: Relationships in knowledge representation and reasoning: GREEN, R.: Internally-structured conceptual models in cognitive semantics; HOVY, E.: Comparing sets of semantic relations in ontologies; GUARINO, N., C. WELTY: Identity and subsumption; JOUIS; C.: Logic of relationships - Pt.3: Applications of relationships: EVENS, M.: Thesaural relations in information retrieval; KHOO, C., S.H. MYAENG: Identifying semantic relations in text for information retrieval and information extraction; McCRAY, A.T., O. BODENREICHER: A conceptual framework for the biiomedical domain; HETZLER, B.: Visual analysis and exploration of relationships
  20. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.01 
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    Date
    14. 8.2004 17:22:56
    Source
    Online. 28(2004) no.3, S.22-29

Authors

Languages

  • e 22
  • d 11

Types

  • a 26
  • m 4
  • el 3
  • s 3
  • x 1
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