We present a novel approach to incorporating semantic information to the problems of natural language processing, in particular to the document classification task. The approach builds on the intuition that semantic relatedness of words can be viewed as a non-static property of the words that depends on the particular task at hand. The semantic relatedness information is incorporated using feat...

Classifying and identifying semantic relations between facts and opinions on the Web is of utmost importance for organizing information on the Web, however, this requires consideration of a broader set of semantic relations than are typically handled in Recognizing Textual Entailment (RTE), Cross-document Structure Theory (CST), and similar tasks. In this paper, we describe the construction and...

The main algorithms at the heart of search engines have focused on ranking and classifying sites. This is appropriate when we know what we are looking for and want it directly. Alternatively, we surf, in which case ranking and classifying links becomes the focus. We address this problem using a latent semantic analysis of the web. This technique allows us to rate, suppress or create links givin...

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a ...

Until recently, little work has been dedicated to the representation and interchange of informal, semi-structured arguments of the type found in natural language prose and dialogue. To redress this, the research community recently initiated work towards an Argument Interchange Format (AIF). The AIF aims to facilitate the exchange of semi-structured arguments among different argument analysis an...

In this paper, we describe our system submitted for the Sentiment Analysis task at SemEval 2013 (Task 2). We implemented a combination of Explicit Semantic Analysis (ESA) with Naive Bayes classifier. ESA represents text as a high dimensional vector of explicitly defined topics, following the distributional semantic model. This approach is novel in the sense that ESA has not been used for Sentim...

INTRODUCTION This paper explores the benefits of using n-grams and semantic features for the classification of disease outbreak reports, in the context of the BioCaster disease outbreak report text mining system. A novel feature of this work is the use of a general purpose semantic tagger - the USAS tagger - to generate features. BACKGROUND We outline the application context for this work (th...

In this paper we consider the problem of identifying and classifying discourse coherence relations. We report initial results over the recently released Discourse GraphBank (Wolf and Gibson, 2005). Our approach considers, and determines the contributions of, a variety of syntactic and lexico-semantic features. We achieve 81% accuracy on the task of discourse relation type classification and 70%...

We ooer a preliminary account of the information-based aspects of punctuation marks. We give our initial treatment within the Discourse Representation Theory and its segmented version. We hypothesize that this work will be useful in classifying the informational contributions of punctuation marks and bringing them to bear on the semantic characterization of written discourse.

Although researchers have shown increasing interest in extracting/classifying semantic relations, most previous studies have basically relied on lexical patterns between terms. This paper proposes a novel way to accomplish the task: a system that captures a physical size of an entity. Experimental results revealed that our proposed method is feasible and prevents the problems inherent in other ...