A general language model for information retrieval
Proceedings of the eighth international conference on Information and knowledge management
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring query models by computing information flow
Proceedings of the eleventh international conference on Information and knowledge management
Dependence language model for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A generative theory of relevance
A generative theory of relevance
Query expansion using term relationships in language models for information retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Latent concept expansion using markov random fields
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Dependency-based syntactic-semantic analysis with PropBank and NomBank
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
| Hi-index | 0.00 |
Bag-of-words approaches to information retrieval (IR) are effective but assume independence between words. The Hyperspace Analogue to Language (HAL) is a cognitively motivated and validated semantic space model that captures statistical dependencies between words by considering their co-occurrences in a surrounding window of text. HAL has been successfully applied to query expansion in IR, but has several limitations, including high processing cost and use of distributional statistics that do not exploit syntax. In this paper, we pursue two methods for incorporating syntactic-semantic information from textual 'events' into HAL. We build the HAL space directly from events to investigate whether processing costs can be reduced through more careful definition of word co-occurrence, and improve the quality of the pseudo-relevance feedback by applying event information as a constraint during HAL construction. Both methods significantly improve performance results in comparison with original HAL, and interpolation of HAL and relevance model expansion outperforms either method alone.