Unsupervised Paraphrase Acquisition via Relation Discovery
نویسندگان
چکیده
One of the difficulties in Natural Language Processing is the fact that there are many way to express the same thing or event. These expressions are called “Paraphrases”. Paraphrase is important in applications such as IR, QA and IE, and one of the difficulties in paraphrase research is acquiring the requisite paraphrase knowledge. In this paper, we describe an unsupervised method to discover paraphrases containing two named entities from a large untagged corpus. The proposed method consists of two stages. First, it finds relations between named entities using similarity of context and clustering. Then, the phrases which express the relation are selected from each cluster to acquire paraphrases. Our experiments with one year of newspaper reveal that we can discover a variety of paraphrases with high precision and high recall.
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