Articles

In this paper, we address the problem of dealing with a large collection of data and propose a method for text classifi- cation which manipulates data using two well-known machine learning techniques, Naive Bayes(NB) and Support Vector Ma- chines(SVMs). NB is based on the as- sumption of word independence in …...

This paper shows that the web can be em- ployed to obtain frequencies for bigrams that are unseen in a given corpus. We describe a method for retrieving counts for adjective-noun, noun-noun, and verb- object bigrams from the web by querying a search engine. We evaluate this method by demonstrating …...

We describe a hybrid approach to improv- ing search performance by providing a natural language front end to a traditional keyword-based search engine. The key component of the system is iterative query formulation and retrieval, in which one or more queries are automatically formulated from the user’s question, issued to …...

An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and cor- responding natural language realizations. Typically, labor-intensive knowledge-based methods are used to construct the dictio- nary. We instead propose to acquire it automatically via a novel multiple-pass al- gorithm employing multiple-sequence align- …...

Text prediction is a form of interactive machine translation that is well suited to skilled translators. In principle it can as- sist in the production of a target text with minimal disruption to a translator’s nor- mal routine. However, recent evaluations of a prototype prediction system showed that it significantly …...

While recent retrieval techniques do not limit the number of index terms, out-of- vocabulary (OOV) words are crucial in speech recognition. Aiming at retrieving information with spoken queries, we fill the gap between speech recognition and text retrieval in terms of the vocabulary size. Given a spoken query, we gener- …...

We present a bootstrapping method that uses strong syntactic heuristics to learn semantic lexicons. The three sources of information are appositives, compound nouns, and ISA clauses. We apply heuris- tics to these syntactic structures, embed them in a bootstrapping architecture, and combine them with co-training. Results on WSJ articles and …...

In the field of empirical natural language processing, researchers constantly deal with large amounts of marked-up data; whether the markup is done by the researcher or someone else, human nature dictates that it will have errors in it. This paper will more fully characterise the problem and discuss whether and …...

We describe and evaluate the application of a spectral clustering technique (Ng et al., 2002) to the unsupervised clustering of German verbs. Our previous work has shown that standard clustering techniques succeed in inducing Levin- style semantic classes from verb subcategorisa- tion information. But clustering in the very high dimensional …...

We present Minimum Bayes-Risk word alignment for machine translation. This statistical, model-based approach attempts to minimize the expected risk of align- ment errors under loss functions that mea- sure alignment quality. We describe var- ious loss functions, including some that incorporate linguistic analysis as can be obtained from parse trees, …...

This paper presents the experiment that was led at the University Paris 7 to teach machine translation to translation students. Linking machine translation with translation-training in order to help students acquire the linguistic and technical skills increasingly required on the translation market has become part of a number of translation …...

This paper discusses the role that can be played in teaching machine translation (MT) by free on-line MT services, emphasising the advantages that they offer for didactic purposes as well as their drawbacks in comparison with traditional PC-based MT software. Some of the key reasons behind the huge potential for …...

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not rec- ommended (thumbs down). The classifi- cation of a review is predicted by the average semantic orientation of the phrases in the review that contain adjec- tives or adverbs. A phrase has a …...

We describe an approach to improve the bilingual cooccurrence dictionary that is used for word alignment, and evaluate the improved dictionary using a version of the Competitive Linking algorithm. We demonstrate a problem faced by the Com- petitive Linking algorithm and present an approach to ameliorate it. In particular, we …...

Named entity phrases are some of the most difficult phrases to translate because new phrases can appear from nowhere, and because many are domain specific, not to be found in bilingual dictionaries. We present a novel algorithm for translating named entity phrases using easily obtain- able monolingual and bilingual resources. …...

Spoken dialogue systems promise effi- cient and natural access to information services from any phone. Recently, spo- ken dialogue systems for widely used ap- plications such as email, travel informa- tion, and customer care have moved from research labs into commercial use. These applications can receive millions of calls a …...

In this paper, we investigate the practical applicability of Co-Training for the task of building a classifier for reference reso- lution. We are concerned with the ques- tion if Co-Training can significantly re- duce the amount of manual labeling work and still produce a classifier with an ac- ceptable performance. …...

This paper compares a number of gen- erative probability models for a wide- coverage Combinatory Categorial Gram- mar (CCG) parser. These models are trained and tested on a corpus obtained by translating the Penn Treebank trees into CCG normal-form derivations. According to an evaluation of unlabeled word-word dependencies, our best …...

This paper describes a wide-coverage sta- tistical parser that uses Combinatory Cat- egorial Grammar (CCG) to derive de- pendency structures. The parser differs from most existing wide-coverage tree- bank parsers in capturing the long-range dependencies inherent in constructions such as coordination, extraction, raising and control, as well as the standard …...

Categorial grammar has traditionally used the λ-calculus to represent meaning. We present an alternative, dependency-based perspective on linguistic meaning and sit- uate it in the computational setting. This perspective is formalized in terms of hy- brid logic and has a rich yet perspicuous propositional ontology that enables a wide variety …...