Exploring and measuring dependency trees for informationretrieval
Chang Liu · 2008
Natural language processing techniques are believed to hold a tremendous potential to supplement the purely quantitative methods of text information retrieval. This has led to the emergence of a large number of NLP-based IR research projects over the last few years, even though the empirical evidence to support this has often been inadequate. Most contributions of NLP to IR mainly concentrate on document representation and compound term matching strategies. Researchers have noted that the simple term-based representation of document content such as vector representation is usually inadequate for accurate discrimination. The "bag of words" representation does not invoke linguistic considerations and allow modelling of relationships between subsets of words. However, even though a variety of content indicator such as syntactic phrase have been tried and investigated for representing documents rather than single terms in IR systems, the matching strategy over those representation still cannot go beyond traditional statistical techniques that measure term co-occurrence characteristics and proximity in analyzing text structure.