Parsing is a computationally intensive task due to the combinatorial explosion seen in chart parsing algorithms that explore possible parse trees. In this paper, we propose a meth...
This article describes a robust semantic parser that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. The FrameNet cor...
Various methods have been proposed for automatic synonym acquisition, as synonyms are one of the most fundamental lexical knowledge. Whereas many methods are based on contextual c...
We investigate prototype-driven learning for primarily unsupervised grammar induction. Prior knowledge is specified declaratively, by providing a few canonical examples of each ta...
Synchronous Context-Free Grammars (SCFGs) have been successfully exploited as translation models in machine translation applications. When parsing with an SCFG, computational comp...
We present MAGEAD, a morphological analyzer and generator for the Arabic language family. Our work is novel in that it explicitly addresses the need for processing the morphology ...
Statistical language models should improve as the size of the n-grams increases from 3 to 5 or higher. However, the number of parameters and calculations, and the storage requirem...
Le Quan Ha, Philip Hanna, Darryl Stewart, F. Jack ...
Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, a...
One of the challenges in the automatic generation of referring expressions is to identify a set of domain entities coherently, that is, from the same conceptual perspective. We de...
We claim that existing specification languages for tree based grammars fail to adequately support identifier managment. We then show that XMG (eXtensible MetaGrammar) provides a s...