We introduce two Bayesian models for unsupervised semantic role labeling (SRL) task. The
models treat SRL as clustering of syntactic signatures of arguments with clusters …

Abstract Domain dependence of NLP systems is one of the major obstacles to their
application in large-scale text analysis, also restricting the applicability of FrameNet …

We increase the lexical coverage of FrameNet through automatic paraphrasing. We use
crowdsourcing to manually filter out bad paraphrases in order to ensure a high-precision …

Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn
data science techniques critical for tackling cybersecurity challenges, and for the data …

We present a framework for using continuousspace vector representations of word meaning
to derive new vectors representing the meaning of senses listed in a semantic network. It is a …

We propose a non-parametric Bayesian model for unsupervised semantic parsing.
Following Poon and Domingos (2009), we consider a semantic parsing setting where the …

The frame-semantic parsing task is challenging for supervised techniques, even for those
few languages where relatively large amounts of labeled data are available. In this …

Although FrameNet is recognized as one of the most fine-grained lexical databases, its
coverage of lexical units is still limited. To tackle this issue, we propose a two-step frame …

This paper describes the design and implementation of a computational model for Arabic
natural language semantics, a semantic parser for capturing the deep semantic …

In unsupervised semantic role labeling, identifying the role of an argument is usually
informed by its dependency relation with the predicate. In this work, we propose a neural …