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Capabilities to categorize a clause based on the type of situation entity(e.g., events, states and generic statements) the clause introduces to thediscourse can benefit many NLP applications. Observing that the situationentity type of a clause depends on discourse functions the clause plays in aparagraph and the interpretation of discourse functions depends heavily onparagraph-wide contexts, we propose to build context-aware clauserepresentations for predicting situation entity types of clauses. Specifically,we propose a hierarchical recurrent neural network model to read a wholeparagraph at a time and jointly learn representations for all the clauses inthe paragraph by extensively modeling context influences and inter-dependenciesof clauses. Experimental results show that our model achieves thestate-of-the-art performance for clause-level situation entity classificationon the genre-rich MASC+Wiki corpus, which approaches human-level performance.

Building Context-aware Clause Representations for Situation Entity Type Classification