Class-based approach to disambiguating Levin verbs

Lapata and Brew (2004) (hereafter LB04) obtain from untagged texts a statistical prior model that is able to generate class preferences for ambiguous Levin (1993) verbs (hereafter Levin). They also show that their informative priors, incorporated into a Naive Bayes classifier deduced from hand-tagged data, can aid in verb class disambiguation. We reanalyse LB04’s prior model and show that a single factor (the joint probability of class and frame) determines the predominant class for a particular verb in a particular frame. This means that the prior model cannot be sensitive to fine-grained lexical distinctions between different individual verbs falling in the same class. We replicate LB04’s supervised disambiguation experiments on large scale data, using deep parsers rather than the shallow parser of LB04. In addition, we introduce a method for training our classifier without using hand-tagged data. This relies on knowledge of Levin class memberships to move information from u...

• Type: Journal
• Year: 2010
• Where: NLE
• Authors: Jianguo Li, Chris Brew
• Keywords: Hand-tagged Data, NLE 2010, Prior Model, Verb Class