Search (4 results, page 1 of 1)

0.011946027 = product of:
  0.04778411 = sum of:
    0.04778411 = weight(_text_:data in 3502) [ClassicSimilarity], result of:
      0.04778411 = score(doc=3502,freq=4.0), product of:
        0.120893985 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03823278 = queryNorm
        0.3952563 = fieldWeight in 3502, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0625 = fieldNorm(doc=3502)
  0.25 = coord(1/4)

Abstract

Lexical analysis is a fundamental operation in both query processing and automatic indexing, and filtering stoplist words is an important step in the automatic indexing process. Presents basic algorithms and data structures for lexical analysis, and shows how stoplist word removal can be efficiently incorporated into lexical analysis

Source

Information retrieval: data structures and algorithms. Ed.: W.B. Frakes u. R. Baeza-Yates

0.0074662673 = product of:
  0.02986507 = sum of:
    0.02986507 = weight(_text_:data in 5816) [ClassicSimilarity], result of:
      0.02986507 = score(doc=5816,freq=4.0), product of:
        0.120893985 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03823278 = queryNorm
        0.24703519 = fieldWeight in 5816, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5816)
  0.25 = coord(1/4)

Abstract

Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word-level indicative representation and message-level indicative representation hierarchically. In both of the modules, we apply character-level representations, which enables the model to explore morphological features and deal with the out-of-vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real-life data sets indicate that our model outperforms state-of-the-art models from previous studies.

0.0073912274 = product of:
  0.02956491 = sum of:
    0.02956491 = weight(_text_:data in 6681) [ClassicSimilarity], result of:
      0.02956491 = score(doc=6681,freq=2.0), product of:
        0.120893985 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03823278 = queryNorm
        0.24455236 = fieldWeight in 6681, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6681)
  0.25 = coord(1/4)

Abstract

Presents a new approach to text analysis for automating the key phrase indexing process, using artificial intelligence techniques. This mimics the behaviour of human experts by using a rule base consisting of insertion and deletion rules generated by subject-matter experts. The insertion rules are based on the idea that some phrases found in a text imply or trigger other phrases. The deletion rules apply to semantically ambiguous phrases where text presence alone does not determine appropriateness as a key phrase. The insertion and deletion rules are used to transform a list of found phrases to a list of key phrases for indexing a document. Statistical data are provided to demonstrate the performance of this expert rule based system

0.005180014 = product of:
  0.020720055 = sum of:
    0.020720055 = product of:
      0.04144011 = sum of:
        0.04144011 = weight(_text_:22 in 6752) [ClassicSimilarity], result of:
          0.04144011 = score(doc=6752,freq=2.0), product of:
            0.13388468 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03823278 = queryNorm
            0.30952093 = fieldWeight in 6752, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=6752)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

6. 3.1997 16:22:15