Extending Tree Kernels Towards Paragraphs

We extend parse tree kernels from the level of individual sentences towards the level of paragraph to build a framework for learning short texts such as search results and social profile postings. We build a set of extended trees for a paragraph of text from the individual parse trees for sentences. It is performed based on coreferences and Rhetoric Structure relations between the phrases in different sentences. Tree kernel learning is applied to extended trees to take advantage of additional discourse-related information. We evaluate our approach, tracking relevance improvement for multi-sentence search, comparing performances of individual sentence kernels with the ones for extended parse trees. The search problem is formulated as classification of search results into the classes of relevant and irrelevant, learning from the Bing search results, used as a baseline and as a training dataset.