Search (1 results, page 1 of 1)

0.0025239778 = product of:
  0.010095911 = sum of:
    0.010095911 = weight(_text_:information in 3295) [ClassicSimilarity], result of:
      0.010095911 = score(doc=3295,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.16457605 = fieldWeight in 3295, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=3295)
  0.25 = coord(1/4)

Abstract

We present and evaluate a novel technique for learning cross-lingual continuous space models to aid cross-language information retrieval (CLIR). Our model, which is referred to as external-data composition neural network (XCNN), is based on a composition function that is implemented on top of a deep neural network that provides a distributed learning framework. Different from most existing models, which rely only on available parallel data for training, our learning framework provides a natural way to exploit monolingual data and its associated relevance metadata for learning continuous space representations of language. Cross-language extensions of the obtained models can then be trained by using a small set of parallel data. This property is very helpful for resource-poor languages, therefore, we carry out experiments on the English-Hindi language pair. On the conducted comparative evaluation, the proposed model is shown to outperform state-of-the-art continuous space models with statistically significant margin on two different tasks: parallel sentence retrieval and ad-hoc retrieval.

Source

Information processing and management. 53(2017) no.2, S.359-370