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SCIENTIA SINICA Informationis, Volume 48, Issue 11: 1510-1520(2018) https://doi.org/10.1360/N112018-00151

Heterographic pun identification model based on multi-dimensional semantic relationships

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  • ReceivedJun 13, 2018
  • AcceptedSep 10, 2018
  • PublishedNov 14, 2018

Abstract

Identifying heterographic puns is an important branch of humor research, which has gradually developed into a new research area. This paper presents a heterographic pun identification mechanism based on feature sets in four dimensions, namely, semantic transparency, semantic relevance, phonetic expansibility, and syntax feature sets. The semantic transparency feature sets consist of the lexical item statistics and the character length; the syntax feature sets include names, capitalization, tense, part of speech, and location. Nine features of the above four dimensions are added to a binary decision tree to generate a threshold and complete a pun identification with the help of K-means clustering. Using the corpus of the SemEval2017 Task 7, the proposed method achieves satisfactory results, and its F1 value outscores the top one out of all participating teams. The experiment outlined in this paper proves that the taxonomic approach of the binary decision tree algorithm based on four dimensions is effective in identifying heterographic puns. The phonetic expansibility and the syntax feature sets are particularly effective among all other dimensions, which is consistent with our presumption that the phonetic feature plays a bigger role in identifying heterographic puns.


Funded by

国家自然科学基金重点项目(61632011)

国家自然科学基金(61772103)

国家自然科学基金(61702080)

国家社会科学基金一般项目(15BYY028)

辽宁省自然基金(20170540230,2015020017,20170540232)

辽宁省优秀人才项目(LJQ2014127)


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  • Figure 1

    (Color online) Puns recognition algrithm based BDT

  • 1   Table 1The pun recognition results of superimposed all features
    Features Precision (%) Recall (%) Accuracy (%) $F1$ (%)
    Semantic transparency 46.57 28.80 88.83 43.49
    Semantic transparency + semantic relevance 48.88 33.02 89.85 47.22
    Semantic transparency + semantic relevance 62.87 57.12 86.22 68.72
    + phonetic expansibility
    Semantic transparency + semantic relevance 78.48 82.93 86.39 84.62
    + phonetic expansibility + syntax features
    The number one of 2017SemEval [30] 78.37 81.90 87.04 84.39
  • 2   Table 2The recognition results of each dimension features
    Features Precision (%) Recall (%) Accuracy (%) $F1$ (%)
    Semantic relevance 32.02 4.88 98.41 9.30
    Semantic transparency 46.57 28.80 88.83 43.49
    Phonetic expansibility 52.58 39.89 86.37 54.57
    Syntax features 69.61 63.49 91.29 74.90
  • 3   Table 3The recognition results of three syntactical structure features
    Features Precision (%) Recall (%) Accuracy (%) $F1$ (%)
    Names 42.75 20.61 96.32 33.96
    Capitalization 30.56 3.93 76.92 7.49
    Tense 67.08 58.22 93.03 71.64

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