论文信息 - CMU-AT: Semantic Distance and Background Knowledge for Identifying Semantic Relations

CMU-AT: Semantic Distance and Background Knowledge for Identifying Semantic Relations

This system uses a background knowledge base to identify semantic relations between base noun phrases in English text, as evaluated in SemEval 2007, Task 4. Training data for each relation is converted to statements in the Scone Knowledge Representation Language. At testing time a new Scone statement is created for the sentence under scrutiny, and presence or absence of a relation is calculated by comparing the total semantic distance between the new statement and all positive examples to the total distance between the new statement and all negative examples.

Scott E. Fahlman | Alicia Tribble

[1] Daniel Gildea,et al. Automatic Labeling of Semantic Roles , 2000, ACL.

[2] Andrew Y. Ng,et al. Robust Textual Inference via Graph Matching , 2005, HLT.

[3] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .

[4] Barbara Rosario,et al. Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy , 2001, EMNLP.

[5] Christiane Fellbaum,et al. Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[6] Preslav Nakov,et al. Classification of semantic relations between nominals , 2009, Lang. Resour. Evaluation.

[8] Stan Szpakowicz,et al. Learning Noun-Modifier Semantic Relations with Corpus-based and WordNet-based Features , 2006, AAAI.

[9] Alessandro Moschitti,et al. A Study on Convolution Kernels for Shallow Statistic Parsing , 2004, ACL.

[10] Preslav Nakov,et al. SemEval-2007 Task 04: Classification of Semantic Relations between Nominals , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[11] Alessandro Moschitti,et al. Making Tree Kernels Practical for Natural Language Learning , 2006, EACL.

[12] Alessandro Moschitti,et al. Automatic Learning of Textual Entailments with Cross-Pair Similarities , 2006, ACL.