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SCIENTIA SINICA Informationis, Volume 48, Issue 11: 1533-1545(2018) https://doi.org/10.1360/N112018-00157

Combining entity co-occurrence information and sentence semantic features for relation extraction

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  • ReceivedOct 5, 2018
  • AcceptedOct 30, 2018
  • PublishedNov 14, 2018

Abstract

Relation extraction is one of the most important tasks in information extraction and a key step in knowledge graph construction. The existing relation extraction approaches mostly try to capture semantic features for entity pairs at the sentence level, which might ignore the global context information of the entities in the entire corpus. In this paper, we propose a novel neural network model for relation extraction, named CNSSNN, which combines the information of entity co-occurrences with sentences' semantic features. In this model, we first build an entity co-occurrence network from the corpus. Then, we introduce a network-level attention mechanism to capture network environmental information selectively and generate the corpus-level global context features for the entities. At the same time, we employ a bi-directional gated recurrent unit (bi-GRU) network to extract sentence-level semantic features for entity pairs. Finally, we combine the corpus-level features and the sentence-level features to classify relations. The experimental results, over a manually labeled dataset, show that our approach consistently outperforms other existing approaches in terms of both precision and recall.


Funded by

国家重点研发计划(2018YFC0830200)


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  • Figure 1

    (Color online) Overall framework of CNSSNN

  • Figure 2

    (Color online) P-R curves of different approaches. (a) SemEval; (b) CnNews

  • Table 1   Symbols and their description
    Symbol Description
    $C$ The corpus
    $s$ A sentence in corpus $C$
    $\boldsymbol{S}$ The matrix representation of a sentence $s$
    $w$ A word in a sentence
    $\boldsymbol{w}$ The vector representation of a word $w$
    $e$ An entity
    $\boldsymbol{f}^c$ Corpus-level features
    $\boldsymbol{f}^s$ Sentence-level features
    $\boldsymbol{f}$ Features of entity pair after features combination
  • Table 2   Numbers of samples of each label in the labeled dataset
    Label Number of samples
    “hold" 1031
    “study at" 923
    “work at" 3033
    “others" 4053
    Total 9040
  • Table 3   Numbers of samples of each label in the SemEval dataset
    Label Number of samples
    “others" 1864
    “cause-effect" 1331
    “instrument-agency" 660
    “product-producer" 948
    “content-container" 732
    “entity-origin" 974
    “entity-destination" 1137
    “component-whole" 1253
    “member-collection" 923
    “message-topic" 895
    Total 10717
  • Table 4   Hyper-parameter setting of CNSSNN
    Hyper-parameter u layer_num $q$ batchsize learning_rate $d$ $d_p$
    Value 100 1 64 250 1E$-$4 400 1
  • Table 5   Performance comparison of different relation extraction approaches on all labels
    Model $F1$ on SemEval (%) $F1$ on CnNews (%)
    CNN 80.43 85.32
    CR-CNN 81.09 86.47
    GRU 81.52 86.83
    ATT-GRU 83.69 88.15
    CNSSNN (ours) 85.99 90.34
  • Table 6   Performance comparison of different relation extraction approaches without “other” label
    Model SemEval $F1$ on CnNews (%)
    Precision (%) Recall (%) $F1$(%) Precision (%) Recall (%) $F1$(%)
    CNN 84.00 79.82 81.76 86.79 82.87 84.83
    CR-CNN 84.20 80.82 82.40 86.85 85.99 85.86
    GRU 82.85 81.07 81.94 85.91 86.16 86.21
    ATT-GRU 85.19 83.07 84.06 87.21 87.93 87.58
    CNSSNN (ours) 87.51 85.66 86.56 92.83 86.15 89.72

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