Unsupervised Learning of Verb Argument Structures

A Statistical Generative Model for Unsupervised Learning of Verb Argument Structures

We present a statistical generative model for unsupervised learning of verb argument structures. We use the model in order to automatically induce verb argument structures for a representative set of verbs. Approximately 80% of the induced argument structures are judged correct by human subjects. The structures overlap significantly with those in PropBank; they also exhibit correct patterns of ...

EFL learners' learning of English verb argument structure

Unsupervised Argument Identification for Semantic Role Labeling

The task of Semantic Role Labeling (SRL) is often divided into two sub-tasks: verb argument identification, and argument classification. Current SRL algorithms show lower results on the identification sub-task. Moreover, most SRL algorithms are supervised, relying on large amounts of manually created data. In this paper we present an unsupervised algorithm for identifying verb arguments, where ...

Multilingual Open Information Extraction

Open Information Extraction (OIE) is a recent unsupervised strategy to extract great amounts of basic propositions (verb-based triples) from massive text corpora which scales to Web-size document collections. We propose a multilingual rule-based OIE method that takes as input dependency parses in the CoNLL-X format, identifies argument structures within the dependency parses, and extracts a set...

Acquisition of English Unergative and Unaccusative Structures by Persian EFL Learners

The acquisition of argument structures has been studied by a variety of second language acquisition scholars within the past two decades (Atay, 2010; Can, 2009; Chay, 2006, & Kras, 2007, among others). In the present study, ‘Predicate’ as the most substantial element of a sentence is investigated. There are three English verb-types which seem to be more problematic for Persian EFL learners: (a)...