Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Using machine learning techniques to interpret WH-questions
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Retrieving answers from frequently asked questions pages on the web
Proceedings of the 14th ACM international conference on Information and knowledge management
Finding similar questions in large question and answer archives
Proceedings of the 14th ACM international conference on Information and knowledge management
Parsing and question classification for question answering
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Interrogative reformulation patterns and acquisition of question paraphrases
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Answering complex questions with random walk models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Descriptive question answering in encyclopedia
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Question Answering in Restricted Domains: An Overview
Computational Linguistics
Input feature selection for classification problems
IEEE Transactions on Neural Networks
A graph-based semi-supervised learning for question semantic labeling
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
A new answer analysis approach for Chinese yes-no question
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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This paper presents a CRF (Conditional Random Field) model for Semantic Chunk Annotation in a Chinese Question and Answering System (SCACQA). The model was derived from a corpus of real world questions, which are collected from some discussion groups on the Internet. The questions are supposed to be answered by other people, so some of the questions are very complex. Mutual information was adopted for feature selection. The training data collection consists of 14000 sentences and the testing data collection consists of 4000 sentences. The result shows an F-score of 93.07%.