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Chinese Commonsense Processing

Chinese Commonsense Processing. Presented by Yen-Ling Kuo 2009/3/30. Remember how to build ConceptNet 2…?. The data we collected:. Context Finding Conceptual Analogy Similarity. Dirty Words Filter Swapping List. Link Prediction Add K-Line. Rapport Game Pet Game. subject. subject.

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Chinese Commonsense Processing

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  1. Chinese Commonsense Processing Presented by Yen-Ling Kuo 2009/3/30

  2. Remember how to build ConceptNet 2…? • The data we collected: Context Finding Conceptual Analogy Similarity Dirty Words Filter Swapping List Link Prediction Add K-Line Rapport Game Pet Game subject subject relation 聖誕節 吃大餐 __ 的時候，你會 __ (2, 1, 0) frequency good rank bad rank

  3. Dirty Words Filter • Idea: Spam filter • Machine learning • Matching/Fuzzy hashing • Black list • Attribute selection • 近朱者赤，近墨者黑 • Node degree • Ratio of bad rank • # rank • # neighbors in black list • Distance to black list • Confidence of users Subjects Black list White list Attribute Selection Classification Bad subjects Good subjects

  4. Link Prediction • Idea: Social network link prediction • Application of social network link predication • In Question-Answering Bulletin Board(QABB): 1. Recommend potential answers based on previous communications 2. Predict future hot questions

  5. Link Prediction Methods • Node attribute: not always available • Structural property  Node based topological pattern  Path based topological pattern  

  6. Link prediction in ChineseCommonsense? • Use both node attribute and structural property → Modeled as a supervisedlearning problem • Giveweightstolinksaccordingtofrequencyandgood/badranks. • Node attribute 詞類, relation types • Structural property Distance, Weightedcommon neighbors,WeightedAdamic/Adar,Typesofneighbors,Katz Weighted common neighbors

  7. Context Finding • Determine the context around a concept is useful for building applications. • Context finding is similar to memory search.→ Use spreading activation from a source node to get the contextual neighborhood. 0.1 枕頭 0.1 Ask 睡覺 1 ※Different relation with different weight. 浴室 0.2 0.6 刷牙 0.12 0.6

  8. Conceptual Analogy • Employ structure-mapping methods to get a list of structurally analogous concepts given a source concept. • Structural analogy is not just a measure of semantic distance, ex. “wedding” and “bride”. 狗 貓 貓 有 __ 狗 有 __ 貓 喜歡 __ 狗 喜歡 __ 貓 and 狗 are conceptually analogous concepts. 貓 會 __ 狗 會 __

  9. Reference • Hugo Liu, Push Sing. Commonsense Reasoning in and over Natural Language, Lecture Notes in Computer Science, 2004. • David Liben-Nowell, Jon Kleinberg. The Link Prediction Problem for Social Network, Proceedings of CIKM, 2003. • Tsuyoshi Murata and Sakiko Moriyasu. Link prediction of social networks based on weighted proximity measures, International Conference on Web Intelligence, 2007

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