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
Dependency-based sentence alignment for multiple document summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Using random walks for question-focused sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning to recognize features of valid textual entailments
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
A SVM-Based Ensemble Approach to Multi-Document Summarization
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Complex question answering: unsupervised learning approaches and experiments
Journal of Artificial Intelligence Research
Natural Language Engineering
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Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization. In this paper, we have experimented with one empirical and two unsupervised statistical machine learning techniques: k-means and Expectation Maximization (EM), for computing relative importance of the sentences. However, the performance of these approaches depends entirely on the feature set used and the weighting of these features. We extracted different kinds of features (i.e. lexical, lexical semantic, cosine similarity, basic element, tree kernel based syntactic and shallow-semantic) for each of the document sentences in order to measure its importance and relevancy to the user query. We used a local search technique to learn the weights of the features. For all our methods of generating summaries, we have shown the effects of syntactic and shallow-semantic features over the bag of words (BOW) features.