Search (24 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Computerlinguistik"
  • × year_i:[2000 TO 2010}
  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. Schneider, J.W.; Borlund, P.: ¬A bibliometric-based semiautomatic approach to identification of candidate thesaurus terms : parsing and filtering of noun phrases from citation contexts (2005) 0.05 
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    Date
    8. 3.2007 19:55:22
    Source
    Context: nature, impact and role. 5th International Conference an Conceptions of Library and Information Sciences, CoLIS 2005 Glasgow, UK, June 2005. Ed. by F. Crestani u. I. Ruthven
  3. Hull, D.; Ait-Mokhtar, S.; Chuat, M.; Eisele, A.; Gaussier, E.; Grefenstette, G.; Isabelle, P.; Samulesson, C.; Segand, F.: Language technologies and patent search and classification (2001) 0.02 
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  4. Rosemblat, G.; Tse, T.; Gemoets, D.: Adapting a monolingual consumer health system for Spanish cross-language information retrieval (2004) 0.02 
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    Abstract
    This preliminary study applies a bilingual term list (BTL) approach to cross-language information retrieval (CLIR) in the consumer health domain and compares it to a machine translation (MT) approach. We compiled a Spanish-English BTL of 34,980 medical and general terms. We collected a training set of 466 general health queries from MedlinePlus en espaiiol and 488 domainspecific queries from ClinicalTrials.gov translated into Spanish. We submitted the training set queries in English against a test bed of 7,170 ClinicalTrials.gov English documents, and compared MT and BTL against this English monolingual standard. The BTL approach was less effective (F = 0.420) than the MT approach (F = 0.578). A failure analysis of the results led to substitution of BTL dictionary sources and the addition of rudimentary normalisation of plural forms. These changes improved the CLIR effectiveness of the same training set queries (F = 0.474), and yielded comparable results for a test set of new 954 queries (F= 0.484). These results will shape our efforts to support Spanishspeakers' needs for consumer health information currently only available in English.
  5. Liu, S.; Liu, F.; Yu, C.; Meng, W.: ¬An effective approach to document retrieval via utilizing WordNet and recognizing phrases (2004) 0.02 
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  6. 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
  7. Fattah, M. Abdel; Ren, F.: English-Arabic proper-noun transliteration-pairs creation (2008) 0.01 
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    Abstract
    Proper nouns may be considered the most important query words in information retrieval. If the two languages use the same alphabet, the same proper nouns can be found in either language. However, if the two languages use different alphabets, the names must be transliterated. Short vowels are not usually marked on Arabic words in almost all Arabic documents (except very important documents like the Muslim and Christian holy books). Moreover, most Arabic words have a syllable consisting of a consonant-vowel combination (CV), which means that most Arabic words contain a short or long vowel between two successive consonant letters. That makes it difficult to create English-Arabic transliteration pairs, since some English letters may not be matched with any romanized Arabic letter. In the present study, we present different approaches for extraction of transliteration proper-noun pairs from parallel corpora based on different similarity measures between the English and romanized Arabic proper nouns under consideration. The strength of our new system is that it works well for low-frequency proper noun pairs. We evaluate the new approaches presented using two different English-Arabic parallel corpora. Most of our results outperform previously published results in terms of precision, recall, and F-Measure.
  8. Martínez, F.; Martín, M.T.; Rivas, V.M.; Díaz, M.C.; Ureña, L.A.: Using neural networks for multiword recognition in IR (2003) 0.01 
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  9. Sebastiani, F.: Machine learning in automated text categorization (2002) 0.01 
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  10. Galvez, C.; Moya-Anegón, F. de; Solana, V.H.: Term conflation methods in information retrieval : non-linguistic and linguistic approaches (2005) 0.01 
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  11. Galvez, C.; Moya-Anegón, F. de: ¬An evaluation of conflation accuracy using finite-state transducers (2006) 0.01 
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  12. Ahmed, F.; Nürnberger, A.: Evaluation of n-gram conflation approaches for Arabic text retrieval (2009) 0.01 
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  13. Zhang, C.; Zeng, D.; Li, J.; Wang, F.-Y.; Zuo, W.: Sentiment analysis of Chinese documents : from sentence to document level (2009) 0.01 
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  14. 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
  15. Hammwöhner, R.: TransRouter revisited : Decision support in the routing of translation projects (2000) 0.01 
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    Date
    10.12.2000 18:22:35
  16. Paolillo, J.C.: Linguistics and the information sciences (2009) 0.01 
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    Date
    27. 8.2011 14:22:33
  17. Li, W.; Wong, K.-F.; Yuan, C.: Toward automatic Chinese temporal information extraction (2001) 0.01 
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  18. Ibekwe-SanJuan, F.; SanJuan, E.: From term variants to research topics (2002) 0.01 
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  19. Peng, F.; Huang, X.: Machine learning for Asian language text classification (2007) 0.01 
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  20. Shaalan, K.; Raza, H.: NERA: Named Entity Recognition for Arabic (2009) 0.01 
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    Abstract
    Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products revolving around natural language processing tasks. Many researchers have attacked the name identification problem in a variety of languages, but only a few limited research efforts have focused on named entity recognition for Arabic script. This is due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this article, we present the results of our attempt at the recognition and extraction of the 10 most important categories of named entities in Arabic script: the person name, location, company, date, time, price, measurement, phone number, ISBN, and file name. We developed the system Named Entity Recognition for Arabic (NERA) using a rule-based approach. The resources created are: a Whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. A filtration mechanism is used that serves two different purposes: (a) revision of the results from a named entity extractor by using metadata, in terms of a Blacklist or rejecter, about ill-formed named entities and (b) disambiguation of identical or overlapping textual matches returned by different name entity extractors to get the correct choice. In NERA, we addressed major challenges posed by NER in the Arabic language arising due to the complexity of the language, peculiarities in the Arabic orthographic system, nonstandardization of the written text, ambiguity, and lack of resources. NERA has been effectively evaluated using our own tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.

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