This research is focused on applying lexical semantics in parsing and PP attachment tasks.

Lexical semantics in parsing

Other notable examples of the successful incorporation of lexical semantics into parsing, not through word sense information but indirectly via selectional preferences, are Dowding et al.

This simple method allows us to incorporate lexical semantic information into the parser, without having to reimplement a full statistical parser.

The results are highly significant in demonstrating that a simplistic approach to incorporating lexical semantics into a parser significantly improves parser performance.

The fact that the improvement is larger for PP attachment than for full parsing is suggestive of PP attachment being a parsing subtask where lexical semantic information is particularly important, supporting the findings of Stetina and Nagao (1997) over a standalone PP attachment task.

Our hope is that this paper serves as the bridgehead for a new line of research into the impact of lexical semantics on parsing.

With any lexical semantic resource, we have to be careful to choose the appropriate level of granularity for a given task: if we limit ourselves to synsets we will not be able to capture broader gen-eralisations, such as the one between knife and scissors;1 on the other hand by grouping words related at a higher level in the hierarchy we could find that we make overly coarse groupings (e.g.

Our approach to exploring the impact of lexical semantics on parsing performance is to take two state-of-the-art statistical treebank parsers and pre-process the inputs variously.

Given our simple procedure for incorporating lexical semantics into the parsing process, our hope is that this research will open the door to further gains using more sophisticated parsing models and richer semantic options.

The performance gain obtained here is larger than in parsing, which is in accordance with the findings of Stetina and Nagao that lexical semantics has a considerable effect on PP attachment