SCIENCE CHINA Information Sciences, Volume 62, Issue 11: 212101(2019) https://doi.org/10.1007/s11432-018-9833-1

Event co-reference resolution via a multi-loss neural network without using argument information

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  • ReceivedNov 16, 2018
  • AcceptedMar 15, 2019
  • PublishedOct 9, 2019


Event co-reference resolution is an important task in natural language processing, and nearly all the existing approaches for this task rely on event argument information. However, these methods tend to suffer from error propagation from event argument extraction. Additionally, not every event mention contains all arguments of an event, and the argument information may confuse the model where events contain arguments to detect an event co-reference in real text. Furthermore, the context information of an event is useful to infer the co-reference between events. Thus, to reduce the errors propagated from event argument extraction and use context information effectively, we propose a multi-loss neural network model that does not require any argument information relating to the within-document event co-reference resolution task; furthermore, it achieves a significantly better performance than the state-of-the-art methods.


This work was supported by National Natural Science Foundation of China (Grant Nos. 61533018, 61806201, 61702512), Independent Research Project of National Laboratory of Pattern Recognition. This work was also supported by CCF-Tencent Open Fund.


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  • Figure 1

    (Color online) Instances of event co-reference resolution.

  • Figure 2

    (Color online) Structure of feedforward neural network for event mention extraction.

  • Figure 3

    (Color online) Structure of MLNN for event co-reference detection.

  • Table 1   Mentions of event components in ECB+ corpus
    Action Participant Time Location
    Shooting Worker/2 women 8:30 p.m. Kraft
  • Table 2   Statistics of ECB+ corpus
    Train Dev. Test Total
    #Document 462 73 447 982
    #Sentences 7294 649 7867 15810
    #Event mentions 3555 441 3290 7268
    #WD chains 2499 316 2137 4953
    Average WD chain length 2.8 2.6 2.6 2.7
  • Table 3   Results of within-document event co-reference resolution on ECB+ corpus
    $B^3$MUCCEAFE$_e$CoNLL $F_1$
    R P $F_1$ R P $F_1$ R P $F_1$ $F_1$
    LEMMA 56.8 80.9 66.7 35.9 76.2 48.8 67.4 62.9 65.1 60.2
    HDP-LEX (2010) 67.6 74.7 71.0 39.1 50.0 43.9 71.4 66.2 68.7 61.2
    Agglomerative (2009) 67.6 80.7 73.5 39.2 61.9 48.0 76.0 65.6 70.4 63.9
    HDDCRP (2015) 67.3 85.6 75.4 41.7 74.3 53.4 79.8 65.1 71.7 66.8
    Iter-WD/CD (2017) 69.2 76.0 72.4 58.5 67.3 62.6 67.9 76.1 71.8 68.9
    MLNN 87.3 71.0 78.3 69.0 57.0 62.4 66.6 76.0 70.7 70.4
  • Table 4   Comparisons of three systems
    $B^3$MUCCEAFE$_e$CoNLL $F_1$
    R P $F_1$ R P $F_1$ R P $F_1$ $F_1$
    C-NN 90.2 48.8 63.3 76.8 40.0 56.0 40.2 69.7 51.0 56.8
    C-MLNN 86.8 67.7 76.0 67.6 53.3 59.6 62.3 74.5 67.9 67.8
    MLNN 87.3 71.0 78.3 69.0 57.0 62.4 66.6 76.0 70.7 70.4

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