Joseph Rosenzweig

Framework and Results for English SENSEVAL

Senseval was the first open, community-based evaluation exercisefor Word Sense Disambiguation programs. It adopted the quantitativeapproach to evaluation developed in MUC and other ARPA evaluationexercises. It took place in 1998. In this paper we describe thestructure, organisation and results of the SENSEVAL exercise forEnglish. We present and defend various design choices for theexercise, describe the data and gold-standard preparation, considerissues of scoring strategies and baselines, and present the resultsfor the 18 participating systems. The exercise identifies thestate-of-the-art for fine-grained word sense disambiguation, wheretraining data is available, as 74–78% correct, with a number ofalgorithms approaching this level of performance. For systems thatdid not assume the availability of training data, performance wasmarkedly lower and also more variable. Human inter-tagger agreementwas high, with the gold standard taggings being around 95%replicable.

Investigating Regular Sense Extensions based on Intersective Levin Classes

In this paper we specifically address questions of polysemy with respect to verbs, and how regular extensions of meaning can be achieved through the adjunction of particular syntactic phrases. We see verb classes as the key to making generalizations about regular extensions of meaning. Current approaches to English classification, Levin classes and WordNet, have limitations in their applicability that impede their utility as general classification schemes. We present a refinement of Levin classes, intersective sets, which are a more fine-grained classification and have more coherent sets of syntactic frames and associated semantic components. We have preliminary indications that the membership of our intersective sets will be more compatible with WordNet than the original Levin classes. We also have begun to examine related classes in Portuguese, and find that these verbs demonstrate similarly coherent syntactic and semantic properties.

Automatic predicate argument analysis of the Penn TreeBank

One of the primary tasks of Information Extraction is recognizing all of the different guises in which a particular type of event can appear. For instance, a meeting between two dignitaries can be referred to as A meets B or A and B meet, or a meeting between A and B took place/was held/opened/convened/finished/dragged on or A had/presided over a meeting/conference with B

Capturing Motion Verb Generalizations in Synchronous Tree Adjoining Grammars

This chapter describes the use of verb class memberships expressed as semantic features as a means of capturing generalizations about manner-of-motion verbs in Synchronous Tree Adjoining Grammars, STAGs, (Shieber and Schabes, 1990; Shieber and Schabes, 1991), which characterize transductions between Lexicalized Tree Adjoining Grammars, LTAG, (Joshi, 1985; Schabes et al., 1988). Our approach allows STAGs, which are essentially transfer-based, to take advantage of the same types of generalizations which are generally thought of as wholly the domain of interlingua systems, without giving up any of the lexical specificity unique to transfer-based systems.