Due to the commonality in natural language, negation focus plays a critical role in deep understanding of context. However, existing studies for negation focus identification major on supervised learning which is timeconsuming and expensive due to manual preparation of annotated corpus. To address this problem, we propose an unsupervised word-topic graph model to represent and measure the focus candidates from both lexical and topic perspectives. Moreover, we propose a document-sensitive biased PageRank algorithm to optimize the ranking scores of focus candidates. Evaluation on the *SEM 2012 shared task corpus shows that our proposed method outperforms the state of the art on negation focus identification.
Whereas both CLaCs NegFocus and FOC-DET use only information from within the sentence, REF argue that contextual discourse information plays a critical role in negation focus identification and propose a word-topic graph model that uses this contextual information from both lexical and topical perspectives.
Unsupervised Negation Focus Identification with Word-Topic Graph Model
EMNLP
Computer Science