Articles

Most existing information retrieval (IR) systems do not take much advantage of natural language processing (NLP) tech- niques due to the complexity and limited observed effectiveness of applying NLP to IR. In this paper, we demonstrate that substantial gains can be obtained over a strong baseline using NLP techniques, if …...

In previous research in automatic verb classification, syntactic features have proved the most useful features, although manual classifications rely heavily on se- mantic features. We show, in contrast with previous work, that considerable ad- ditional improvement can be obtained by using semantic features in automatic clas- sification: verb selectional preferences …...

Distance-based (windowless) word asso- cation measures have only very recently appeared in the NLP literature and their performance compared to existing win- dowed or frequency-based measures is largely unknown. We conduct a large- scale empirical comparison of a variety of distance-based and frequency-based mea- sures for the reproduction of syntagmatic …...

Mitchell et al. (2008) demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI. This could be a very powerful technique for evaluating conceptual models extracted from corpora; however, fMRI is expensive and imposes strong constraints on data collection. Following on experiments that demonstrated that …...

We develop a general method to match un- structured text reviews to a structured list of objects. For this, we propose a lan- guage model for generating reviews that incorporates a description of objects and a generic review language model. This mix- ture model gives us a principled method to …...

We extend previous work on fully unsu- pervised part-of-speech tagging. Using a non-parametric version of the HMM, called the infinite HMM (iHMM), we ad- dress the problem of choosing the number of hidden states in unsupervised Markov models for PoS tagging. We experi- ment with two non-parametric priors, the Dirichlet …...

Sentiment analysis often relies on a se- mantic orientation lexicon of positive and negative words. A number of approaches have been proposed for creating such lex- icons, but they tend to be computation- ally expensive, and usually rely on signifi- cant manual annotation and large corpora. Most of these methods …...

Polarity lexicons have been a valuable re- source for sentiment analysis and opinion mining. There are a number of such lexi- cal resources available, but it is often sub- optimal to use them as is, because general purpose lexical resources do not reflect domain-specific lexical usage. In this pa- per, …...

This paper presents a simple and effective approach to improve dependency parsing by using subtrees from auto-parsed data. First, we use a baseline parser to parse large-scale unannotated data. Then we ex- tract subtrees from dependency parse trees in the auto-parsed data. Finally, we con- struct new subtree-based features for …...

While traditional work on text clustering has largely focused on grouping docu- ments by topic, it is conceivable that a user may want to cluster documents along other dimensions, such as the author’s mood, gender, age, or sentiment. Without know- ing the user’s intention, a clustering al- gorithm will only …...

We present an inexact search algorithm for the problem of predicting a two-layered dependency graph. The algorithm is based on a k-best version of the standard cubic- time search algorithm for projective de- pendency parsing, which is used as the backbone of a beam search procedure. This allows us to …...

This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning ap- proach. We describe an extension of semi- supervised structured conditional models (SS-SCMs) to the dependency parsing problem, whose framework is originally proposed in (Suzuki and Isozaki, 2008). Moreover, we introduce two extensions re- lated …...

In this paper, we investigate how an ac- curate question classifier contributes to a question answering system. We first present a Maximum Entropy (ME) based question classifier which makes use of head word features and their WordNet hy- pernyms. We show that our question clas- sifier can achieve the state …...

The ambiguity of person names in the Web has become a new area of interest for NLP researchers. This challenging problem has been formulated as the task of clustering Web search results (returned in response to a person name query) according to the individual they mention. In this paper we …...

User clicks on a URL in response to a query are extremely useful predictors of the URL’s rele- vance to that query. Exact match click features tend to suffer from severe data sparsity issues in web ranking. Such sparsity is particularly pro- nounced for new URLs or long queries where …...

This paper explores two classes of model adapta- tion methods for Web search ranking: Model In- terpolation and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very …...

This paper addresses the issue of extract- ing contexts and answers of questions from post discussion of online forums. We propose a novel and unified model by customizing the structural Support Vector Machine method. Our customization has several attractive properties: (1) it gives a comprehensive graphical representation of thread discussion. …...

The recently introduced online confidence-weighted (CW) learning algorithm for binary classification per- forms well on many binary NLP tasks. However, for multi-class problems CW learning updates and inference cannot be computed analytically or solved as convex optimization problems as they are in the binary case. We derive learning algorithms for …...

Statistical bilingual word alignment has been well studied in the context of machine trans- lation. This paper adapts the bilingual word alignment algorithm to monolingual scenario to extract collocations from monolingual cor- pus. The monolingual corpus is first repli- cated to generate a parallel corpus, where each sentence pair consists …...

In this paper, we present an algorithm for ex- tracting translations of any given multiword expression from parallel corpora. Given a multiword expression to be translated, the method involves extracting a short list of tar- get candidate words from parallel corpora based on scores of normalized frequency, generating possible translations …...