An Effective Approach for Finding Comparative Sentence Pairs from Contrastive Opinioned Text
| Autor: | Hsin-Lan Wang, 王馨蘭 |
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| Rok vydání: | 2011 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 99 In this thesis, the opinioned reviews from web forum are used as the data source. Our goal is to provide an effective approach for automatically summarizing comparative sentence pairs from contractive opinioned text. Users usually give comments for a product on its features or functions, whose part of speech usually belong to nouns. Accordingly, each opinioned sentence is characterized by a noun feature vector according to the nouns appearing in the sentence. For the purpose of gathering the sentences describing on the same topic, clustering is performed on the opinioned sentences according to their noun feature vectors. Then, for each cluster, the positive and negative sentences are separated into two groups. In each group, after constructing the association graph of sentences according to their similarity degree, the representative score of each sentence is computed. For each positive and negative pairs selected from a cluster, the comparative score of the pair is obtained by performing a weighted sum to combine the representative scores of the two sentences and the similarity degree between the two sentences. The pair with the highest comparative score in a cluster will be selected as a comparative sentence pair. Moreover, we propose an efficient updating algorithm to insert a new opinioned sentence into the existing clusters of sentences incrementally. Then, it only requires performing comparative sentence pair selection from the updated cluster. The experimental results show that the effectiveness of the comparative sentence pair extraction method proposed in this thesis outperforms the related work. Especially, the proposed cluster updating algorithm has significant improvement on execution efficiency for processing newly inserted opinioned sentences. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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