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Intra-sentential Zero Anaphora Resolution using Subject Sharing Recognition
"... In this work, we improve the performance of intra-sentential zero anaphora resolu-tion in Japanese using a novel method of recognizing subject sharing relations. In Japanese, a large portion of intra-sentential zero anaphora can be regarded as subject sharing relations between pred-icates, that is, ..."
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In this work, we improve the performance of intra-sentential zero anaphora resolu-tion in Japanese using a novel method of recognizing subject sharing relations. In Japanese, a large portion of intra-sentential zero anaphora can be regarded as subject sharing relations between pred-icates, that is, the subject of some predi-cate is also the unrealized subject of other predicates. We develop an accurate rec-ognizer of subject sharing relations for pairs of predicates in a single sentence, and then construct a subject shared pred-icate network, which is a set of predi-cates that are linked by the subject shar-ing relations recognized by our recognizer. We finally combine our zero anaphora resolution method exploiting the subject shared predicate network and a state-of-the-art ILP-based zero anaphora resolution method. Our combined method achieved a significant improvement over the the ILP-based method alone on intra-sentential zero anaphora resolution in Japanese. To the best of our knowledge, this is the first work to explicitly use an independent sub-ject sharing recognizer in zero anaphora resolution. 1
Discovering Concept-Level Event Associations from a Text Stream
"... Abstract. We study an open text mining problem -discovering concept-level event associations from a text stream. We investigate the importance and challenge of this task and propose a novel solution by using event sequential patterns. The proposed approach can discover important event associations ..."
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Abstract. We study an open text mining problem -discovering concept-level event associations from a text stream. We investigate the importance and challenge of this task and propose a novel solution by using event sequential patterns. The proposed approach can discover important event associations implicitly expressed. The discovered event associations are general and useful as knowledge for applications such as event prediction.
