Search (2 results, page 1 of 1)

  • × author_ss:"Wilbur, W.J."
  • × theme_ss:"Computerlinguistik"
  • × year_i:[2000 TO 2010}
  1. Comeau, D.C.; Wilbur, W.J.: Non-Word Identification or Spell Checking Without a Dictionary (2004) 0.00 
    0.0033657318 = product of:
  0.0067314636 = sum of:
    0.0067314636 = product of:
      0.013462927 = sum of:
        0.013462927 = weight(_text_:a in 2092) [ClassicSimilarity], result of:
          0.013462927 = score(doc=2092,freq=22.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.25351265 = fieldWeight in 2092, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=2092)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    MEDLINE is a collection of more than 12 million references and abstracts covering recent life science literature. With its continued growth and cutting-edge terminology, spell-checking with a traditional lexicon based approach requires significant additional manual followup. In this work, an internal corpus based context quality rating a, frequency, and simple misspelling transformations are used to rank words from most likely to be misspellings to least likely. Eleven-point average precisions of 0.891 have been achieved within a class of 42,340 all alphabetic words having an a score less than 10. Our models predict that 16,274 or 38% of these words are misspellings. Based an test data, this result has a recall of 79% and a precision of 86%. In other words, spell checking can be done by statistics instead of with a dictionary. As an application we examine the time history of low a words in MEDLINE titles and abstracts.
    Type
    a
  2. Kim, W.; Wilbur, W.J.: Corpus-based statistical screening for content-bearing terms (2001) 0.00 
    0.0027894354 = product of:
  0.005578871 = sum of:
    0.005578871 = product of:
      0.011157742 = sum of:
        0.011157742 = weight(_text_:a in 5188) [ClassicSimilarity], result of:
          0.011157742 = score(doc=5188,freq=34.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.21010503 = fieldWeight in 5188, product of:
              5.8309517 = tf(freq=34.0), with freq of:
                34.0 = termFreq=34.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=5188)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Kim and Wilber present three techniques for the algorithmic identification in text of content bearing terms and phrases intended for human use as entry points or hyperlinks. Using a set of 1,075 terms from MEDLINE evaluated on a zero to four, stop word to definite content word scale, they evaluate the ranked lists of their three methods based on their placement of content words in the top ranks. Data consist of the natural language elements of 304,057 MEDLINE records from 1996, and 173,252 Wall Street Journal records from the TIPSTER collection. Phrases are extracted by breaking at punctuation marks and stop words, normalized by lower casing, replacement of nonalphanumerics with spaces, and the reduction of multiple spaces. In the ``strength of context'' approach each document is a vector of binary values for each word or word pair. The words or word pairs are removed from all documents, and the Robertson, Spark Jones relevance weight for each term computed, negative weights replaced with zero, those below a randomness threshold ignored, and the remainder summed for each document, to yield a score for the document and finally to assign to the term the average document score for documents in which it occurred. The average of these word scores is assigned to the original phrase. The ``frequency clumping'' approach defines a random phrase as one whose distribution among documents is Poisson in character. A pvalue, the probability that a phrase frequency of occurrence would be equal to, or less than, Poisson expectations is computed, and a score assigned which is the negative log of that value. In the ``database comparison'' approach if a phrase occurring in a document allows prediction that the document is in MEDLINE rather that in the Wall Street Journal, it is considered to be content bearing for MEDLINE. The score is computed by dividing the number of occurrences of the term in MEDLINE by occurrences in the Journal, and taking the product of all these values. The one hundred top and bottom ranked phrases that occurred in at least 500 documents were collected for each method. The union set had 476 phrases. A second selection was made of two word phrases occurring each in only three documents with a union of 599 phrases. A judge then ranked the two sets of terms as to subject specificity on a 0 to 4 scale. Precision was the average subject specificity of the first r ranks and recall the fraction of the subject specific phrases in the first r ranks and eleven point average precision was used as a summary measure. The three methods all move content bearing terms forward in the lists as does the use of the sum of the logs of the three methods.
    Type
    a

Authors