Search (47 results, page 1 of 3)

  • × language_ss:"e"
  • × theme_ss:"Computerlinguistik"
  • × year_i:[2000 TO 2010}
  1. Airio, E.: Who benefits from CLIR in web retrieval? (2008) 0.11 
    0.11020951 = product of:
  0.14694601 = sum of:
    0.060458954 = weight(_text_:web in 2342) [ClassicSimilarity], result of:
      0.060458954 = score(doc=2342,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.37471575 = fieldWeight in 2342, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2342)
    0.03959212 = weight(_text_:search in 2342) [ClassicSimilarity], result of:
      0.03959212 = score(doc=2342,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.230407 = fieldWeight in 2342, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=2342)
    0.04689494 = product of:
      0.09378988 = sum of:
        0.09378988 = weight(_text_:engine in 2342) [ClassicSimilarity], result of:
          0.09378988 = score(doc=2342,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.35462496 = fieldWeight in 2342, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=2342)
      0.5 = coord(1/2)
  0.75 = coord(3/4)
    Abstract
    Purpose - The aim of the current paper is to test whether query translation is beneficial in web retrieval. Design/methodology/approach - The language pairs were Finnish-Swedish, English-German and Finnish-French. A total of 12-18 participants were recruited for each language pair. Each participant performed four retrieval tasks. The author's aim was to compare the performance of the translated queries with that of the target language queries. Thus, the author asked participants to formulate a source language query and a target language query for each task. The source language queries were translated into the target language utilizing a dictionary-based system. In English-German, also machine translation was utilized. The author used Google as the search engine. Findings - The results differed depending on the language pair. The author concluded that the dictionary coverage had an effect on the results. On average, the results of query-translation were better than in the traditional laboratory tests. Originality/value - This research shows that query translation in web is beneficial especially for users with moderate and non-active language skills. This is valuable information for developers of cross-language information retrieval systems.
  2. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.10 
    0.10014303 = product of:
  0.13352405 = sum of:
    0.078522965 = product of:
      0.23556888 = sum of:
        0.23556888 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
          0.23556888 = score(doc=562,freq=2.0), product of:
            0.41914827 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.049439456 = queryNorm
            0.56201804 = fieldWeight in 562, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=562)
      0.33333334 = coord(1/3)
    0.03490599 = weight(_text_:web in 562) [ClassicSimilarity], result of:
      0.03490599 = score(doc=562,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.21634221 = fieldWeight in 562, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=562)
    0.02009509 = product of:
      0.04019018 = sum of:
        0.04019018 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
          0.04019018 = score(doc=562,freq=2.0), product of:
            0.17312855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049439456 = queryNorm
            0.23214069 = fieldWeight in 562, 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=562)
      0.5 = coord(1/2)
  0.75 = coord(3/4)
    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
  3. Chandrasekar, R.; Bangalore, S.: Glean : using syntactic information in document filtering (2002) 0.09 
    0.093985304 = product of:
  0.12531374 = sum of:
    0.029088326 = weight(_text_:web in 4257) [ClassicSimilarity], result of:
      0.029088326 = score(doc=4257,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.18028519 = fieldWeight in 4257, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4257)
    0.057146307 = weight(_text_:search in 4257) [ClassicSimilarity], result of:
      0.057146307 = score(doc=4257,freq=6.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.33256388 = fieldWeight in 4257, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4257)
    0.03907912 = product of:
      0.07815824 = sum of:
        0.07815824 = weight(_text_:engine in 4257) [ClassicSimilarity], result of:
          0.07815824 = score(doc=4257,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.29552078 = fieldWeight in 4257, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4257)
      0.5 = coord(1/2)
  0.75 = coord(3/4)
    Abstract
    In today's networked world, a huge amount of data is available in machine-processable form. Likewise, there are any number of search engines and specialized information retrieval (IR) programs that seek to extract relevant information from these data repositories. Most IR systems and Web search engines have been designed for speed and tend to maximize the quantity of information (recall) rather than the relevance of the information (precision) to the query. As a result, search engine users get inundated with information for practically any query, and are forced to scan a large number of potentially relevant items to get to the information of interest. The Holy Grail of IR is to somehow retrieve those and only those documents pertinent to the user's query. Polysemy and synonymy - the fact that often there are several meanings for a word or phrase, and likewise, many ways to express a conceptmake this a very hard task. While conventional IR systems provide usable solutions, there are a number of open problems to be solved, in areas such as syntactic processing, semantic analysis, and user modeling, before we develop systems that "understand" user queries and text collections. Meanwhile, we can use tools and techniques available today to improve the precision of retrieval. In particular, using the approach described in this article, we can approximate understanding using the syntactic structure and patterns of language use that is latent in documents to make IR more effective.
  4. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.09 
    0.09054339 = product of:
  0.120724514 = sum of:
    0.050382458 = weight(_text_:web in 2541) [ClassicSimilarity], result of:
      0.050382458 = score(doc=2541,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.3122631 = fieldWeight in 2541, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2541)
    0.046659768 = weight(_text_:search in 2541) [ClassicSimilarity], result of:
      0.046659768 = score(doc=2541,freq=4.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.27153727 = fieldWeight in 2541, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2541)
    0.02368229 = product of:
      0.04736458 = sum of:
        0.04736458 = weight(_text_:22 in 2541) [ClassicSimilarity], result of:
          0.04736458 = score(doc=2541,freq=4.0), product of:
            0.17312855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049439456 = queryNorm
            0.27358043 = fieldWeight in 2541, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2541)
      0.5 = coord(1/2)
  0.75 = coord(3/4)
    Abstract
    The Specialized Information Services Division (SIS) of the National Library of Medicine (NLM) provides Web access to more than a dozen scientific databases on toxicology and the environment on TOXNET . Search queries on TOXNET often include misspelled or variant English words, medical and scientific jargon and chemical names. Following the example of search engines like Google and ClinicalTrials.gov, we set out to develop a spelling "suggestion" system for increased recall and precision in TOXNET searching. This paper describes development of dictionary technology that can be used in a variety of applications such as orthographic verification, writing aid, natural language processing, and information storage and retrieval. The design of the technology allows building complex applications using the components developed in the earlier phases of the work in a modular fashion without extensive rewriting of computer code. Since many of the potential applications envisioned for this work have on-line or web-based interfaces, the dictionaries and other computer components must have fast response, and must be adaptable to open-ended database vocabularies, including chemical nomenclature. The dictionary vocabulary for this work was derived from SIS and other databases and specialized resources, such as NLM's Unified Medical Language Systems (UMLS) . The resulting technology, A-Z Dictionary (AZdict), has three major constituents: 1) the vocabulary list, 2) the word attributes that define part of speech and morphological relationships between words in the list, and 3) a set of programs that implements the retrieval of words and their attributes, and determines similarity between words (ChemSpell). These three components can be used in various applications such as spelling verification, spelling aid, part-of-speech tagging, paraphrasing, and many other natural language processing functions.
    Date
    14. 8.2004 17:22:56
    Source
    Online. 28(2004) no.3, S.22-29
  5. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.06 
    0.06451727 = product of:
  0.12903453 = sum of:
    0.060458954 = weight(_text_:web in 3455) [ClassicSimilarity], result of:
      0.060458954 = score(doc=3455,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.37471575 = fieldWeight in 3455, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=3455)
    0.068575576 = weight(_text_:search in 3455) [ClassicSimilarity], result of:
      0.068575576 = score(doc=3455,freq=6.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.39907667 = fieldWeight in 3455, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=3455)
  0.5 = coord(2/4)
    Abstract
    Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this article, we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines, and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR), uses proximity and question type features and achieves a total reciprocal document rank of .20 an the TREC8 corpus. Our techniques have been implemented as a Web-accessible system, called NSIR.
  6. Wang, F.L.; Yang, C.C.: Mining Web data for Chinese segmentation (2007) 0.06 
    0.061094895 = product of:
  0.12218979 = sum of:
    0.06504348 = weight(_text_:web in 604) [ClassicSimilarity], result of:
      0.06504348 = score(doc=604,freq=10.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.40312994 = fieldWeight in 604, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=604)
    0.057146307 = weight(_text_:search in 604) [ClassicSimilarity], result of:
      0.057146307 = score(doc=604,freq=6.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.33256388 = fieldWeight in 604, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=604)
  0.5 = coord(2/4)
    Abstract
    Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
  7. Navarretta, C.; Pedersen, B.S.; Hansen, D.H.: Language technology in knowledge-organization systems (2006) 0.06 
    0.057735257 = product of:
  0.11547051 = sum of:
    0.068575576 = weight(_text_:search in 5706) [ClassicSimilarity], result of:
      0.068575576 = score(doc=5706,freq=6.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.39907667 = fieldWeight in 5706, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=5706)
    0.04689494 = product of:
      0.09378988 = sum of:
        0.09378988 = weight(_text_:engine in 5706) [ClassicSimilarity], result of:
          0.09378988 = score(doc=5706,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.35462496 = fieldWeight in 5706, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=5706)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    This paper describes the language technology methods developed in the Danish research project VID to extract from Danish text material relevant information for the population of knowledge organization systems (KOS) within specific corporate domains. The results achieved by applying these methods to a prototype search engine tuned to the patent and trademark domain indicate that the use of human language technology can support the construction of a linguistically based KOS and that linguistic information in search improves recall substantially without harming precision (near 90%). Finally, we describe two research experiments where (1) linguistic analysis of Danish compounds and is exploited to improve search atrategies on these (2) linguistic knowledge is used to model corporate knowledge into a language-based ontology.
  8. Thelwall, M.; Price, L.: Language evolution and the spread of ideas on the Web : a procedure for identifying emergent hybrid word (2006) 0.05 
    0.050025538 = product of:
  0.100051075 = sum of:
    0.060458954 = weight(_text_:web in 5896) [ClassicSimilarity], result of:
      0.060458954 = score(doc=5896,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.37471575 = fieldWeight in 5896, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=5896)
    0.03959212 = weight(_text_:search in 5896) [ClassicSimilarity], result of:
      0.03959212 = score(doc=5896,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.230407 = fieldWeight in 5896, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=5896)
  0.5 = coord(2/4)
    Abstract
    Word usage is of interest to linguists for its own sake as well as to social scientists and others who seek to track the spread of ideas, for example, in public debates over political decisions. The historical evolution of language can be analyzed with the tools of corpus linguistics through evolving corpora and the Web. But word usage statistics can only be gathered for known words. In this article, techniques are described and tested for identifying new words from the Web, focusing on the case when the words are related to a topic and have a hybrid form with a common sequence of letters. The results highlight the need to employ a combination of search techniques and show the wide potential of hybrid word family investigations in linguistics and social science.
  9. Li, Q.; Chen, Y.P.; Myaeng, S.-H.; Jin, Y.; Kang, B.-Y.: Concept unification of terms in different languages via web mining for Information Retrieval (2009) 0.05 
    0.045585044 = product of:
  0.09117009 = sum of:
    0.05817665 = weight(_text_:web in 4215) [ClassicSimilarity], result of:
      0.05817665 = score(doc=4215,freq=8.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.36057037 = fieldWeight in 4215, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4215)
    0.032993436 = weight(_text_:search in 4215) [ClassicSimilarity], result of:
      0.032993436 = score(doc=4215,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.19200584 = fieldWeight in 4215, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4215)
  0.5 = coord(2/4)
    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.
  10. Nait-Baha, L.; Jackiewicz, A.; Djioua, B.; Laublet, P.: Query reformulation for information retrieval on the Web using the point of view methodology : preliminary results (2001) 0.04 
    0.037249055 = product of:
  0.07449811 = sum of:
    0.03490599 = weight(_text_:web in 249) [ClassicSimilarity], result of:
      0.03490599 = score(doc=249,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.21634221 = fieldWeight in 249, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=249)
    0.03959212 = weight(_text_:search in 249) [ClassicSimilarity], result of:
      0.03959212 = score(doc=249,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.230407 = fieldWeight in 249, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=249)
  0.5 = coord(2/4)
    Abstract
    The work we are presenting is devoted to the information collected on the WWW. By the term collected we mean the whole process of retrieving, extracting and presenting results to the user. This research is part of the RAP (Research, Analyze, Propose) project in which we propose to combine two methods: (i) query reformulation using linguistic markers according to a given point of view; and (ii) text semantic analysis by means of contextual exploration results (Descles, 1991). The general project architecture describing the interactions between the users, the RAP system and the WWW search engines is presented in Nait-Baha et al. (1998). We will focus this paper on showing how we use linguistic markers to reformulate the queries according to a given point of view
  11. Peis, E.; Herrera-Viedma, E.; Herrera, J.C.: On the evaluation of XML documents using Fuzzy linguistic techniques (2003) 0.04 
    0.037249055 = product of:
  0.07449811 = sum of:
    0.03490599 = weight(_text_:web in 2778) [ClassicSimilarity], result of:
      0.03490599 = score(doc=2778,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.21634221 = fieldWeight in 2778, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2778)
    0.03959212 = weight(_text_:search in 2778) [ClassicSimilarity], result of:
      0.03959212 = score(doc=2778,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.230407 = fieldWeight in 2778, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=2778)
  0.5 = coord(2/4)
    Abstract
    Recommender systems evaluate and filter the great amount of information available an the Web to assist people in their search processes. A fuzzy evaluation method of XML documents based an computing with words is presented. Given an XML document type (e.g. scientific article), we consider that its elements are not equally informative. This is indicated by the use of a DTD and defining linguistic importance attributes to the more meaningful elements of the DTD designed. Then, the evaluation method generates linguistic recommendations from linguistic evaluation judgements provided by different recommenders an meaningful elements of DTD.
  12. Cimiano, P.; Völker, J.; Studer, R.: Ontologies on demand? : a description of the state-of-the-art, applications, challenges and trends for ontology learning from text (2006) 0.04 
    0.037249055 = product of:
  0.07449811 = sum of:
    0.03490599 = weight(_text_:web in 6014) [ClassicSimilarity], result of:
      0.03490599 = score(doc=6014,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.21634221 = fieldWeight in 6014, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=6014)
    0.03959212 = weight(_text_:search in 6014) [ClassicSimilarity], result of:
      0.03959212 = score(doc=6014,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.230407 = fieldWeight in 6014, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=6014)
  0.5 = coord(2/4)
    Abstract
    Ontologies are nowadays used for many applications requiring data, services and resources in general to be interoperable and machine understandable. Such applications are for example web service discovery and composition, information integration across databases, intelligent search, etc. The general idea is that data and services are semantically described with respect to ontologies, which are formal specifications of a domain of interest, and can thus be shared and reused in a way such that the shared meaning specified by the ontology remains formally the same across different parties and applications. As the cost of creating ontologies is relatively high, different proposals have emerged for learning ontologies from structured and unstructured resources. In this article we examine the maturity of techniques for ontology learning from textual resources, addressing the question whether the state-of-the-art is mature enough to produce ontologies 'on demand'.
  13. Peng, F.; Huang, X.: Machine learning for Asian language text classification (2007) 0.04 
    0.03706527 = product of:
  0.07413054 = sum of:
    0.041137107 = weight(_text_:web in 831) [ClassicSimilarity], result of:
      0.041137107 = score(doc=831,freq=4.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.25496176 = fieldWeight in 831, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=831)
    0.032993436 = weight(_text_:search in 831) [ClassicSimilarity], result of:
      0.032993436 = score(doc=831,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.19200584 = fieldWeight in 831, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=831)
  0.5 = coord(2/4)
    Abstract
    Purpose - The purpose of this research is to compare several machine learning techniques on the task of Asian language text classification, such as Chinese and Japanese where no word boundary information is available in written text. The paper advocates a simple language modeling based approach for this task. Design/methodology/approach - Naïve Bayes, maximum entropy model, support vector machines, and language modeling approaches were implemented and were applied to Chinese and Japanese text classification. To investigate the influence of word segmentation, different word segmentation approaches were investigated and applied to Chinese text. A segmentation-based approach was compared with the non-segmentation-based approach. Findings - There were two findings: the experiments show that statistical language modeling can significantly outperform standard techniques, given the same set of features; and it was found that classification with word level features normally yields improved classification performance, but that classification performance is not monotonically related to segmentation accuracy. In particular, classification performance may initially improve with increased segmentation accuracy, but eventually classification performance stops improving, and can in fact even decrease, after a certain level of segmentation accuracy. Practical implications - Apply the findings to real web text classification is ongoing work. Originality/value - The paper is very relevant to Chinese and Japanese information processing, e.g. webpage classification, web search.
  14. Sprachtechnologie, mobile Kommunikation und linguistische Ressourcen : Beiträge zur GLDV Tagung 2005 in Bonn (2005) 0.03 
    0.034910798 = product of:
  0.069821596 = sum of:
    0.030229477 = weight(_text_:web in 3578) [ClassicSimilarity], result of:
      0.030229477 = score(doc=3578,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.18735787 = fieldWeight in 3578, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3578)
    0.03959212 = weight(_text_:search in 3578) [ClassicSimilarity], result of:
      0.03959212 = score(doc=3578,freq=8.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.230407 = fieldWeight in 3578, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3578)
  0.5 = coord(2/4)
    Content
    INHALT: Chris Biemann/Rainer Osswald: Automatische Erweiterung eines semantikbasierten Lexikons durch Bootstrapping auf großen Korpora - Ernesto William De Luca/Andreas Nürnberger: Supporting Mobile Web Search by Ontology-based Categorization - Rüdiger Gleim: HyGraph - Ein Framework zur Extraktion, Repräsentation und Analyse webbasierter Hypertextstrukturen - Felicitas Haas/Bernhard Schröder: Freges Grundgesetze der Arithmetik: Dokumentbaum und Formelwald - Ulrich Held/ Andre Blessing/Bettina Säuberlich/Jürgen Sienel/Horst Rößler/Dieter Kopp: A personalized multimodal news service -Jürgen Hermes/Christoph Benden: Fusion von Annotation und Präprozessierung als Vorschlag zur Behebung des Rohtextproblems - Sonja Hüwel/Britta Wrede/Gerhard Sagerer: Semantisches Parsing mit Frames für robuste multimodale Mensch-Maschine-Kommunikation - Brigitte Krenn/Stefan Evert: Separating the wheat from the chaff- Corpus-driven evaluation of statistical association measures for collocation extraction - Jörn Kreutel: An application-centered Perspective an Multimodal Dialogue Systems - Jonas Kuhn: An Architecture for Prallel Corpusbased Grammar Learning - Thomas Mandl/Rene Schneider/Pia Schnetzler/Christa Womser-Hacker: Evaluierung von Systemen für die Eigennamenerkennung im crosslingualen Information Retrieval - Alexander Mehler/Matthias Dehmer/Rüdiger Gleim: Zur Automatischen Klassifikation von Webgenres - Charlotte Merz/Martin Volk: Requirements for a Parallel Treebank Search Tool - Sally YK. Mok: Multilingual Text Retrieval an the Web: The Case of a Cantonese-Dagaare-English Trilingual e-Lexicon -
    Darja Mönke: Ein Parser für natürlichsprachlich formulierte mathematische Beweise - Martin Müller: Ontologien für mathematische Beweistexte - Moritz Neugebauer: The status of functional phonological classification in statistical speech recognition - Uwe Quasthoff: Kookkurrenzanalyse und korpusbasierte Sachgruppenlexikographie - Reinhard Rapp: On the Relationship between Word Frequency and Word Familiarity - Ulrich Schade/Miloslaw Frey/Sebastian Becker: Computerlinguistische Anwendungen zur Verbesserung der Kommunikation zwischen militärischen Einheiten und deren Führungsinformationssystemen - David Schlangen/Thomas Hanneforth/Manfred Stede: Weaving the Semantic Web: Extracting and Representing the Content of Pathology Reports - Thomas Schmidt: Modellbildung und Modellierungsparadigmen in der computergestützten Korpuslinguistik - Sabine Schröder/Martina Ziefle: Semantic transparency of cellular phone menus - Thorsten Trippel/Thierry Declerck/Ulrich Held: Standardisierung von Sprachressourcen: Der aktuelle Stand - Charlotte Wollermann: Evaluation der audiovisuellen Kongruenz bei der multimodalen Sprachsynsthese - Claudia Kunze/Lothar Lemnitzer: Anwendungen des GermaNet II: Einleitung - Claudia Kunze/Lothar Lemnitzer: Die Zukunft der Wortnetze oder die Wortnetze der Zukunft - ein Roadmap-Beitrag -
    Karel Pala: The Balkanet Experience - Peter M. Kruse/Andre Nauloks/Dietmar Rösner/Manuela Kunze: Clever Search: A WordNet Based Wrapper for Internet Search Engines - Rosmary Stegmann/Wolfgang Woerndl: Using GermaNet to Generate Individual Customer Profiles - Ingo Glöckner/Sven Hartrumpf/Rainer Osswald: From GermaNet Glosses to Formal Meaning Postulates -Aljoscha Burchardt/ Katrin Erk/Anette Frank: A WordNet Detour to FrameNet - Daniel Naber: OpenThesaurus: ein offenes deutsches Wortnetz - Anke Holler/Wolfgang Grund/Heinrich Petith: Maschinelle Generierung assoziativer Termnetze für die Dokumentensuche - Stefan Bordag/Hans Friedrich Witschel/Thomas Wittig: Evaluation of Lexical Acquisition Algorithms - Iryna Gurevych/Hendrik Niederlich: Computing Semantic Relatedness of GermaNet Concepts - Roland Hausser: Turn-taking als kognitive Grundmechanik der Datenbanksemantik - Rodolfo Delmonte: Parsing Overlaps - Melanie Twiggs: Behandlung des Passivs im Rahmen der Datenbanksemantik- Sandra Hohmann: Intention und Interaktion - Anmerkungen zur Relevanz der Benutzerabsicht - Doris Helfenbein: Verwendung von Pronomina im Sprecher- und Hörmodus - Bayan Abu Shawar/Eric Atwell: Modelling turn-taking in a corpus-trained chatbot - Barbara März: Die Koordination in der Datenbanksemantik - Jens Edlund/Mattias Heldner/Joakim Gustafsson: Utterance segmentation and turn-taking in spoken dialogue systems - Ekaterina Buyko: Numerische Repräsentation von Textkorpora für Wissensextraktion - Bernhard Fisseni: ProofML - eine Annotationssprache für natürlichsprachliche mathematische Beweise - Iryna Schenk: Auflösung der Pronomen mit Nicht-NP-Antezedenten in spontansprachlichen Dialogen - Stephan Schwiebert: Entwurf eines agentengestützten Systems zur Paradigmenbildung - Ingmar Steiner: On the analysis of speech rhythm through acoustic parameters - Hans Friedrich Witschel: Text, Wörter, Morpheme - Möglichkeiten einer automatischen Terminologie-Extraktion.
  15. Bian, G.-W.; Chen, H.-H.: Cross-language information access to multilingual collections on the Internet (2000) 0.03 
    0.03472981 = product of:
  0.06945962 = sum of:
    0.049364526 = weight(_text_:web in 4436) [ClassicSimilarity], result of:
      0.049364526 = score(doc=4436,freq=4.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.3059541 = fieldWeight in 4436, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=4436)
    0.02009509 = product of:
      0.04019018 = sum of:
        0.04019018 = weight(_text_:22 in 4436) [ClassicSimilarity], result of:
          0.04019018 = score(doc=4436,freq=2.0), product of:
            0.17312855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049439456 = 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)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    Language barrier is the major problem that people face in searching for, retrieving, and understanding multilingual collections on the Internet. This paper deals with query translation and document translation in a Chinese-English information retrieval system called MTIR. Bilingual dictionary and monolingual corpus-based approaches are adopted to select suitable tranlated query terms. A machine transliteration algorithm is introduced to resolve proper name searching. We consider several design issues for document translation, including which material is translated, what roles the HTML tags play in translation, what the tradeoff is between the speed performance and the translation performance, and what from the translated result is presented in. About 100.000 Web pages translated in the last 4 months of 1997 are used for quantitative study of online and real-time Web page translation
    Date
    16. 2.2000 14:22:39
  16. Hoenkamp, E.; Bruza, P.D.; Song, D.; Huang, Q.: ¬An effective approach to verbose queries using a limited dependencies language model (2009) 0.03 
    0.028829023 = product of:
  0.057658046 = sum of:
    0.026394749 = weight(_text_:search in 2122) [ClassicSimilarity], result of:
      0.026394749 = score(doc=2122,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.15360467 = fieldWeight in 2122, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.03125 = fieldNorm(doc=2122)
    0.031263296 = product of:
      0.06252659 = sum of:
        0.06252659 = weight(_text_:engine in 2122) [ClassicSimilarity], result of:
          0.06252659 = score(doc=2122,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.23641664 = fieldWeight in 2122, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.03125 = fieldNorm(doc=2122)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    Intuitively, any 'bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.
  17. Yang, C.C.; Luk, J.: Automatic generation of English/Chinese thesaurus based on a parallel corpus in laws (2003) 0.03 
    0.026222898 = product of:
  0.052445795 = sum of:
    0.04072366 = weight(_text_:web in 1616) [ClassicSimilarity], result of:
      0.04072366 = score(doc=1616,freq=8.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.25239927 = fieldWeight in 1616, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1616)
    0.011722136 = product of:
      0.023444273 = sum of:
        0.023444273 = weight(_text_:22 in 1616) [ClassicSimilarity], result of:
          0.023444273 = score(doc=1616,freq=2.0), product of:
            0.17312855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049439456 = queryNorm
            0.1354154 = fieldWeight in 1616, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1616)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    The information available in languages other than English in the World Wide Web is increasing significantly. According to a report from Computer Economics in 1999, 54% of Internet users are English speakers ("English Will Dominate Web for Only Three More Years," Computer Economics, July 9, 1999, http://www.computereconomics. com/new4/pr/pr990610.html). However, it is predicted that there will be only 60% increase in Internet users among English speakers verses a 150% growth among nonEnglish speakers for the next five years. By 2005, 57% of Internet users will be non-English speakers. A report by CNN.com in 2000 showed that the number of Internet users in China had been increased from 8.9 million to 16.9 million from January to June in 2000 ("Report: China Internet users double to 17 million," CNN.com, July, 2000, http://cnn.org/2000/TECH/computing/07/27/ china.internet.reut/index.html). According to Nielsen/ NetRatings, there was a dramatic leap from 22.5 millions to 56.6 millions Internet users from 2001 to 2002. China had become the second largest global at-home Internet population in 2002 (US's Internet population was 166 millions) (Robyn Greenspan, "China Pulls Ahead of Japan," Internet.com, April 22, 2002, http://cyberatias.internet.com/big-picture/geographics/article/0,,5911_1013841,00. html). All of the evidences reveal the importance of crosslingual research to satisfy the needs in the near future. Digital library research has been focusing in structural and semantic interoperability in the past. Searching and retrieving objects across variations in protocols, formats and disciplines are widely explored (Schatz, B., & Chen, H. (1999). Digital libraries: technological advances and social impacts. IEEE Computer, Special Issue an Digital Libraries, February, 32(2), 45-50.; Chen, H., Yen, J., & Yang, C.C. (1999). International activities: development of Asian digital libraries. IEEE Computer, Special Issue an Digital Libraries, 32(2), 48-49.). However, research in crossing language boundaries, especially across European languages and Oriental languages, is still in the initial stage. In this proposal, we put our focus an cross-lingual semantic interoperability by developing automatic generation of a cross-lingual thesaurus based an English/Chinese parallel corpus. When the searchers encounter retrieval problems, Professional librarians usually consult the thesaurus to identify other relevant vocabularies. In the problem of searching across language boundaries, a cross-lingual thesaurus, which is generated by co-occurrence analysis and Hopfield network, can be used to generate additional semantically relevant terms that cannot be obtained from dictionary. In particular, the automatically generated cross-lingual thesaurus is able to capture the unknown words that do not exist in a dictionary, such as names of persons, organizations, and events. Due to Hong Kong's unique history background, both English and Chinese are used as official languages in all legal documents. Therefore, English/Chinese cross-lingual information retrieval is critical for applications in courts and the government. In this paper, we develop an automatic thesaurus by the Hopfield network based an a parallel corpus collected from the Web site of the Department of Justice of the Hong Kong Special Administrative Region (HKSAR) Government. Experiments are conducted to measure the precision and recall of the automatic generated English/Chinese thesaurus. The result Shows that such thesaurus is a promising tool to retrieve relevant terms, especially in the language that is not the same as the input term. The direct translation of the input term can also be retrieved in most of the cases.
    Footnote
    Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"
  18. Notess, G.R.: Up and coming search technologies (2000) 0.02 
    0.023095407 = product of:
  0.09238163 = sum of:
    0.09238163 = weight(_text_:search in 5467) [ClassicSimilarity], result of:
      0.09238163 = score(doc=5467,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.5376164 = fieldWeight in 5467, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.109375 = fieldNorm(doc=5467)
  0.25 = coord(1/4)
  19. 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 
    0.01979606 = product of:
  0.07918424 = sum of:
    0.07918424 = weight(_text_:search in 6318) [ClassicSimilarity], result of:
      0.07918424 = score(doc=6318,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.460814 = fieldWeight in 6318, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.09375 = fieldNorm(doc=6318)
  0.25 = coord(1/4)
  20. Ding, Y.; Chowdhury, G.C.; Foo, S.: Incorporating the results of co-word analyses to increase search variety for information retrieval (2000) 0.02 
    0.01979606 = product of:
  0.07918424 = sum of:
    0.07918424 = weight(_text_:search in 6328) [ClassicSimilarity], result of:
      0.07918424 = score(doc=6328,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.460814 = fieldWeight in 6328, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.09375 = fieldNorm(doc=6328)
  0.25 = coord(1/4)

Authors

Languages

Types

  • a 39
  • m 6
  • s 3
  • el 2
  • More… Less…

Themes

Subjects

Classifications