Mining The Web For Collocations: Ir Models Of Term Associations

Automatic collocation recognition has attracted considerable attention of researchers from diverse fields since it is one of the fundamental tasks in NLP, which feeds into several other tasks (e.g., parsing, idioms, summarization, etc.). Despite this attention the problem has remained a "daunting challenge." As others have observed before, existing approaches based on frequencies and statistical information have limitations. An even bigger problem is that they are restricted to bigrams and as yet there is no consensus on how to extend them to trigrams and higher-order n-grams. This paper presents encouraging results based on novel angles of general collocation extraction leveraging statistics and the Web. In contrast to existing work, our algorithms are applicable to n-grams of arbitrary order, and directional. Experiments across several datasets, including a gold-standard benchmark dataset that we created, demonstrate the effectiveness of proposed methods.