Towards context-aware syntax parsing and tagging

Information retrieval (IR) has become one of the most popular Natural Language Processing (NLP) applications. Part of speech (PoS) parsing and tagging plays an important role in IR systems. A broad range of PoS parsers and taggers tools have been proposed with the aim of helping to find a solution for the information retrieval problems, but most of these are tools based on generic NLP tags which do not capture domain-related information. In this research, we present a domain-specific parsing and tagging approach that uses not only generic PoS tags but also domain-specific PoS tags, grammatical rules, and domain knowledge. Experimental results show that our approach has a good level of accuracy when applying it to different domains

Porting statistical parsers with data-defined kernels

Previous results have shown disappointing performance when porting a parser trained on one domain to another domain where only a small amount of data is available. We propose the use of data-defined kernels as a way to exploit statistics from a source domain while still specializing a parser to a target domain. A probabilistic model trained on the source domain (and possibly also the target domain) is used to define a kernel, which is then used in a large margin classifier trained only on the target domain. With a SVM classifier and a neural network probabilistic model, this method achieves improved performance over the probabilistic model alone.