We compare two types of methods which deal with unknown words in the context of computational grammars. Methods of the first type are based on the idea of supertagging and use a tagger to predict lexical descriptions for unknown tokens in a given input. The second type of methods perform lexical acquisition (LA) which, in the context of this paper, refers to the automatic acquisition of new lex...

We have developed a prototype system for syntactic parsing of corpus text based on a wide-coverage unification-based grammar of English and domain-independent statistical techniques for selecting the most plausible parses from the typically large number licensed by the grammar. Although the results from initial experiments are promising, the system is ‘brittle’, relying particularly on the corr...

This dissertation deals with the robustness problem of deep linguistic processing. Hand-crafted deep linguistic grammars provide precise modeling of human languages, but are deficient in their capability of handling ill-formed or extra-grammatical inputs. In this dissertation, we argue that with a series of robust processing techniques, improved coverage can be achieved without sacrificing effi...

Breadth and Depth of Semantic Lexicons is based on a 1996 workshop. It represents several fundamental lines of thinking that were, and remain, a useful organization of the issues in lexical semantics, and specifically in the organization and development of new generations of lexical corpora. The volume contains very useful background discussions of classic issues in lexical semantics (e.g., Eng...

This paper describes the Spanish Resource Grammar, an open-source multi-purpose broad-coverage precise grammar for Spanish. The grammar is implemented on the Linguistic Knowledge Builder (LKB) system, it is grounded in the theoretical framework of Head-driven Phrase Structure Grammar (HPSG), and it uses Minimal Recursion Semantics (MRS) for the semantic representation. We have developed a hybri...

In this paper we present an innovative hybrid Text Classification (TC) system that bridges the gap between statistical and context based techniques. Our algorithm harnesses contextual information at two stages. First it extracts a cohesive set of keywords for each category by using lexical references, implicit context as derived from LSA and wordvicinity driven semantics. And secondly, each doc...

Text analysis involves the deconstruction of information within a text. This includes text structure, text pattern, linguistic features, lexical analysis, and syntactic analysis. This research took as its starting point the bottom-up approach of analysing the lexical features, syntactic features, and textual features of patent abstracts for comprehensive coverage of text analysis. Several tools...

We present an approach for Semantic Role Labeling (SRL) using Conditional Random Fields in a joint identification/classification step. The approach is based on shallow syntactic information (chunks) and a number of lexicalized features such as selectional preferences and automatically inferred similar words, extracted using lexical databases and distributional similarity metrics. We use semanti...

This work addresses the classification of word pairs as instances of lexical-semantic relations. The classification is approached by leveraging patterns of co-occurrence contexts from corpus data. The significance of using dependency information, of augmenting the set of dependency paths provided to the system, and of generalizing patterns using part-of-speech information for the classification...