In this paper we start to explore two-part collocation extraction association measures that do not estimate expected probabilities on the basis of the independence assumption. We ...
We present a novel method to improve word alignment quality and eventually the translation performance by producing and combining complementary word alignments for low-resource la...
This work deals with the application of confidence measures within an interactivepredictive machine translation system in order to reduce human effort. If a small loss in translat...
Many NLP tasks need accurate knowledge for semantic inference. To this end, mostly WordNet is utilized. Yet WordNet is limited, especially for inference between predicates. To hel...
Semi-supervised word alignment aims to improve the accuracy of automatic word alignment by incorporating full or partial manual alignments. Motivated by standard active learning q...
Maintaining high annotation consistency in large corpora is crucial for statistical learning; however, such work is hard, especially for tasks containing semantic elements. This p...
We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semisupervised metric learning algorithms to those generated by ...
Paramveer S. Dhillon, Partha Pratim Talukdar, Koby...
This paper describes ongoing work on distributional models for word meaning in context. We abandon the usual one-vectorper-word paradigm in favor of an exemplar model that activat...
Supporting natural language input may improve learning in intelligent tutoring systems. However, interpretation errors are unavoidable and require an effective recovery policy. We...
Myroslava Dzikovska, Johanna D. Moore, Natalie B. ...
This paper presents a joint optimization method of a two-step conditional random field (CRF) model for machine transliteration and a fast decoding algorithm for the proposed metho...