In this paper, we propose a novel class of graphs, the tripartite directed acyclic graphs (tDAGs), to model first-order rule feature spaces for sentence pair classification. We in...
We present a system that finds short definitions of terms on Web pages. It employs a Maximum Entropy classifier, but it is trained on automatically generated examples; hence, it i...
Measuring the similarity between two texts is a fundamental problem in many NLP and IR applications. Among the existing approaches, the cosine measure of the term vectors represen...
In this paper, we address the issue of automatic extending lexical resources by exploiting existing knowledge repositories. In particular, we deal with the new task of linking Fra...
Traditional learning-based coreference resolvers operate by training a mentionpair classifier for determining whether two mentions are coreferent or not. Two independent lines of ...
Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retr...
This paper investigates the effect of direction in phrase-based statistial machine translation decoding. We compare a typical phrase-based machine translation decoder using a left...
This paper introduces a new parser evaluation corpus containing around 700 sentences annotated with unbounded dependencies, from seven different grammatical constructions. We run ...
Traditionally, machine learning approaches for information extraction require human annotated data that can be costly and time-consuming to produce. However, in many cases, there ...
In this paper, we present a novel approach for mining opinions from product reviews, where it converts opinion mining task to identify product features, expressions of opinions an...