Search (1 results, page 1 of 1)

0.001425896 = product of:
  0.0128330635 = sum of:
    0.0128330635 = weight(_text_:of in 5132) [ClassicSimilarity], result of:
      0.0128330635 = score(doc=5132,freq=6.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.20947541 = fieldWeight in 5132, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5132)
  0.11111111 = coord(1/9)

Abstract

Ensuring quick and consistent access to large collections of unstructured documents is one of the biggest challenges facing knowledge-intensive organizations. Designing specific vocabularies to index and retrieve documents is often deemed too expensive, full-text search being preferred despite its known limitations. However, the process of creating controlled vocabularies can be partly automated thanks to natural language processing and machine learning techniques. With a case study from the biopharmaceutical industry, we demonstrate how small organizations can use an automated workflow in order to create a controlled vocabulary to index unstructured documents in a semantically meaningful way.