Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Mining term association patterns from search logs for effective query reformulation
Proceedings of the 17th ACM conference on Information and knowledge management
Online expansion of rare queries for sponsored search
Proceedings of the 18th international conference on World wide web
Extracting structured information from user queries with semi-supervised conditional random fields
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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With evolving Web, short length parallel corpora is becoming very common and some of these include user queries, web snippets etc. This paper concerns situations where short length parallel corpora has to be analyzed in order to find meaningful unit-alignment. This is similar to dealing with parallel corpora where a sentence level alignment of translations is required, but differs in that the alignment is to be inferred at unit (word or phrase) level. A Conditional Random Field (CRF) based approach is proposed to discover this unit alignment. Given pairs of semantically or syntactically similar entities, the problem is formulated as that of mutual segmentation and sequence alignment problem. The mutual segmentation refers to the process of segmenting the first entity based on units (or labels) in the second entity and vice-versa. The process of optimizing this mutual segmentation also results in optimal unit alignment. Since our training data is not segmented and unit-aligned, we modify the CRF objective function to accommodate unsupervised data and iterative learning. We have applied this framework to Web Search domain and specifically for query reformulation task. Finally, our experiments suggest that the proposed approach indeed results in meaningful alternatives of the original query.