SMIR 2014 - Workshop on Semantic Matching in Information Retrieval

Recently, significant progress has been made in research on what we call semantic matching (SM), in Web search, question answering, online advertisement, cross language information retrieval, multimedia retrieval, and other tasks. Let us take Web search as example of the problem. When comparing the textual content of query and documents, the simple term-based approaches can fail when searcher and author use different terms. A more realistic approach beyond bag-of-words, referred to as semantic matching (SM), is to conduct deeper query and document analysis to encode text with richer representations and then perform query-document matching with such representations. The main purpose of the workshop is to bring together IR and NLP researchers working on or interested in semantic matching, to share latest research results, express opinions on the related issues, and discuss future directions.
Julio Gonzalo is head of nlp.uned.es, the UNED research group in Natural Language Processing and Information Retrieval. His research interests include Evaluation Methodologies and Metrics in Information Retrieval and Natural Language Processing, Cross-Language and Interactive Information Retrieval, Search Results Organization, Entity-Oriented and Semantic Search.
Hang Li is chief scientist of the Noah's Ark Lab at Huawei. His research areas include information retrieval, natural language processing, statistical machine learning, and data mining.
Alessandro Moschitti is a Senior Research Scientist at the Qatar Computing Research Institute (QCRI) and a tenured professor at the CS Department of the University of Trento, Italy. He has significant expertise in both theoretical and applied machine learning for NLP, IR and Data Mining.
Jun Xu is a researcher at Noah's Ark Lab, Huawei, Hong Kong. His research interest focuses on machine learning for web search and text mining.