In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product difference combine the information in individual sentences. Experimental results show that our model outperforms existing sentence encoding-based approaches by a large margin.
The simplest approach to match two sentences, perhaps, is to concatenate their vector representations [Arc-I] DRR,CNN:NIPS . Concatenation is also applied in our previous work of matching the subject and object in relation classification @cite_1 @cite_0 . apply additional heuristics, namely Euclidean distance, cosine measure, and element-wise absolute difference. The above methods operate on a fixed-size vector representation of a sentence, categorized as -based approaches. Thus the matching complexity is @math , i.e., independent of the sentence length. Word-by-word similarity matrices are introduced to enhance interaction. To obtain the similarity matrix, (Arc-II) concatenate two words' vectors (after convolution), compute Euclidean distance, and apply tensor product. In this way, the complexity is of @math , where @math is the length of a sentence; hence similarity matrices are difficult to scale and less efficient for large datasets.
@cite_0 @cite_1
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Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolutional neural networks or recurrent networks). They may fail to explore the potential representation space in different abstraction levels. In this paper, we propose deep recurrent neural networks (DRNNs) for relation classification to tackle this challenge. Further, we propose a data augmentation method by leveraging the directionality of relations. We evaluated our DRNNs on the SemEval-2010 Task 8, and achieve an F1-score of 86.1 , outperforming previous state-of-the-art recorded results. Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an @math -score of 83.7 , higher than competing methods in the literature.