Comparing knowledge sources for nominal anaphora resolution

We compare two ways of obtaining lexical knowledge for antecedent selection in other-anaphora and definite noun phrase coreference. Specifically, we compare an algorithm that relies on links encoded in the manually created lexical hierarchy WordNet and an algorithm that mines corpora by means of shallow lexico-semantic patterns. As corpora we use the British National Corpus (BNC), as well as the Web, which has not been previously used for this task. Our results show that (a) the knowledge encoded in WordNet is often insufficient, especially for anaphor-antecedent relations that exploit subjective or context-dependent knowledge; (b) for other-anaphora, the Web-based method outperforms the WordNet-based method; (c) for definite NP coreference, the Web-based method yields results comparable to those obtained using WordNet over the whole dataset and outperforms the WordNet-based method on subsets of the dataset; (d) in both case studies, the BNC-based method is worse than the other methods because of data sparseness. Thus, in our studies, the Web-based method alleviated the lexical knowledge gap often encountered in anaphora resolution, and handled examples with context-dependent relations between anaphor and antecedent. Because it is inexpensive and needs no hand-modelling of lexical knowledge, it is a promising knowledge source to integrate in anaphora resolution systems

Can Subcategorisation Probabilities Help a Statistical Parser?

Research into the automatic acquisition of lexical information from corpora is starting to produce large-scale computational lexicons containing data on the relative frequencies of subcategorisation alternatives for individual verbal predicates. However, the empirical question of whether this type of frequency information can in practice improve the accuracy of a statistical parser has not yet been answered. In this paper we describe an experiment with a wide-coverage statistical grammar and parser for English and subcategorisation frequencies acquired from ten million words of text which shows that this information can significantly improve parse accuracy.Comment: 9 pages, uses colacl.st

Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

Corpus Annotation for Parser Evaluation

We describe a recently developed corpus annotation scheme for evaluating parsers that avoids shortcomings of current methods. The scheme encodes grammatical relations between heads and dependents, and has been used to mark up a new public-domain corpus of naturally occurring English text. We show how the corpus can be used to evaluate the accuracy of a robust parser, and relate the corpus to extant resources.Comment: 7 pages, LaTeX (uses eaclap.sty