Search (2 results, page 1 of 1)

  • × author_ss:"Gómez-Rodríguez, C."
  • × year_i:[2010 TO 2020}
  1. Vilares, D.; Alonso, M.A.; Gómez-Rodríguez, C.: On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages (2015) 0.00 
    0.0030344925 = product of:
  0.01213797 = sum of:
    0.01213797 = weight(_text_:information in 2161) [ClassicSimilarity], result of:
      0.01213797 = score(doc=2161,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13714671 = fieldWeight in 2161, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2161)
  0.25 = coord(1/4)
    Abstract
    Millions of micro texts are published every day on Twitter. Identifying the sentiment present in them can be helpful for measuring the frame of mind of the public, their satisfaction with respect to a product, or their support of a social event. In this context, polarity classification is a subfield of sentiment analysis focused on determining whether the content of a text is objective or subjective, and in the latter case, if it conveys a positive or a negative opinion. Most polarity detection techniques tend to take into account individual terms in the text and even some degree of linguistic knowledge, but they do not usually consider syntactic relations between words. This article explores how relating lexical, syntactic, and psychometric information can be helpful to perform polarity classification on Spanish tweets. We provide an evaluation for both shallow and deep linguistic perspectives. Empirical results show an improved performance of syntactic approaches over pure lexical models when using large training sets to create a classifier, but this tendency is reversed when small training collections are used.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1799-1816
  2. Doval, Y.; Gómez-Rodríguez, C.: Comparing neural- and N-gram-based language models for word segmentation (2019) 0.00 
    0.0030344925 = product of:
  0.01213797 = sum of:
    0.01213797 = weight(_text_:information in 4675) [ClassicSimilarity], result of:
      0.01213797 = score(doc=4675,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13714671 = fieldWeight in 4675, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4675)
  0.25 = coord(1/4)
    Abstract
    Word segmentation is the task of inserting or deleting word boundary characters in order to separate character sequences that correspond to words in some language. In this article we propose an approach based on a beam search algorithm and a language model working at the byte/character level, the latter component implemented either as an n-gram model or a recurrent neural network. The resulting system analyzes the text input with no word boundaries one token at a time, which can be a character or a byte, and uses the information gathered by the language model to determine if a boundary must be placed in the current position or not. Our aim is to use this system in a preprocessing step for a microtext normalization system. This means that it needs to effectively cope with the data sparsity present on this kind of texts. We also strove to surpass the performance of two readily available word segmentation systems: The well-known and accessible Word Breaker by Microsoft, and the Python module WordSegment by Grant Jenks. The results show that we have met our objectives, and we hope to continue to improve both the precision and the efficiency of our system in the future.
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.2, S.187-197

Authors

Themes