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Experiments of Opinion Analysis On MPQA and NTCIR-6

Experiments of Opinion Analysis On MPQA and NTCIR-6. Yaoyong Li, Kalina Bontcheva, Hamish Cunningham Department of Computer Science University of Sheffield {yaoyong,kalina,hamish}@dcs.shef.ac.uk http://gate.ac.uk/ http://nlp.shef.ac.uk/.

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Experiments of Opinion Analysis On MPQA and NTCIR-6

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  1. Experiments of Opinion Analysis On MPQA and NTCIR-6 Yaoyong Li, Kalina Bontcheva, Hamish Cunningham Department of Computer Science University of Sheffield {yaoyong,kalina,hamish}@dcs.shef.ac.uk http://gate.ac.uk/http://nlp.shef.ac.uk/

  2. We participated two tracks, English and Chinese corpora. Compared the results on the MPQA corpus and the NTCIR-6 corpus. Outlines 2(10)

  3. Uni-gram of token’s lemma and POS tf*idf representation of sentence. SVM with uneven margins as binary classifier. Opinionated Sentence Recognition 3(10)

  4. An information extraction problem. Identify the first token and last token of an opinion holder. Two SVM binary classifiers. Opinion Holder Extraction 4(10)

  5. Consists of 535 news articles. 360 documents were used for training and other 175 documents for testing. Experiments on MPQA Corpus 5(10)

  6. Results on MPQA Corpus • There are comparable with the state of the art results published. 6(10)

  7. Results on NTCIR-6 English • Using the SVM models learned from the MPQA corpus. • The following are the official results of the run GATE-1. 7(10)

  8. GATE-1 Results Using GATE Evaluation Tools • Results of the opinionated sentence recognition became lower. • Results of the opinion holder extraction was a slightly higher. 8(10)

  9. 300 documents for training, and 139 documents for testing. Just use the annotations of one annotator, in the file “OAT2006 formalrun english a1.csv”. 212 opinion holders (among the 2355 opinion holders) in the file which had no match within the corresponding sentences. We made necessary changes on them to find the text. Experiments Using NTCIR-6 English Corpus for Training and Testing 9(10)

  10. Results Using NTCIR-6 English Corpus for Training and Testing • Much improved results by using the NTCIR-6 corpus for training and testing, showing that there really exist differences between the two corpora, • Still worse than the results on the MPQA corpus. 10(10)

  11. SVM with uneven margins obtained state of the art results on the MPQA corpus. On NTCIR corpus, obtained moderate results on opinionated sentence extraction, but poor results on opinion holder. Using NTCIR-6 English corpus for training and testing obtained much improved results, but were still worse than those on MPQA. Conclusions 11(10)

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