This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles. 1 Introduction and Related Work

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.

Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool which includes feature-enhanced Naïve Bayes, Cosine, Decision List, Transformation-based Learning and MMVC classifiers. Each classifier has access to the same rich feature space, comprised of distance weighted bag-of-lemmas, local ngram context and specific syntactic relations, such as Verb-Object and Noun-Modifier.

This article presents a comprehensive empirical exploration and evaluation of a diverse range of data characteristics which inuence word sense disambiguation performance. It focuses on a set of 6 core supervised algorithms, including 3 variants of Bayesian classifiers, a cosine model, non-hierarchical decision lists, and an extension of the transformation-based learning model. Performance is investigated in detail with respect to the following parameters: (a) target language (English, Spanish, Swedish and Basque), (b) part of speech, (c) sense granularity, (d) inclusion and exclusion of major feature classes, (e) variable context width (further broken down by part-of-speech of keyword), (f) number of training examples, (g) baseline probability of the most likely sense, (h) sense distributional entropy, (i) number of senses per keyword, (j) divergence between training and test data, (k) degree of (artificially introduced) noise in the training data, (l) the effectiveness of an algorithm's confidence rankings, and (m) a full keyword breakdown of the performance of each algorithm. The article concludes with a brief analysis of similarities, differences, strengths and weaknesses of the algorithms and a hierarchical clustering of these algorithms based on agreement of sense classification behavior. Collectively, the article constitutes the most comprehensive survey of evaluation measures and tests yet applied to sense disambiguation algorithms. And it does so over a diverse range of supervised algorithms, languages and parameter spaces in single unified experimental framework.

Word Sense Disambiguation suffers from a long-standing problem of knowledge acquisition bottleneck. Although state of the art supervised systems report good accuracies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an approach for learning coarser and more general set of concepts from a sense tagged corpus, in order to alleviate the knowledge acquisition bottleneck. We show that these general concepts can be transformed to fine grained word senses using simple heuristics, and applying the technique for recent SENSEVAL data sets shows that our approach can yield state of the art performance. 1

Word sense ambiguity is recognized as having a detrimental effect on the precision of information retrieval systems in general and web search systems in particular, due to the sparse nature of the queries involved. Despite continued research into the application of automated word sense disambiguation, the question remains as to whether less than 90 % accurate automated word sense disambiguation can lead to improvements in retrieval effectiveness. In this study we explore the development and subsequent evaluation of a statistical word sense disambiguation system which demonstrates increased precision from a sense based vector space retrieval model over traditional TF*IDF techniques.

This paper demonstrates the substantial empirical success of classifier combination for the word sense disambiguation task. It investigates more than 10 classifier combination methods, including second order classifier stacking, over 6 major structurally different base classifiers (enhanced Nave Bayes, cosine, Bayes Ratio, decision lists, transformationbased learning and maximum variance boosted mixture models). The paper also includes in-depth performance analysis sensitive to properties of the feature space and component classifiers. When evaluated on the standard SENSEVAL1 and 2 data sets on 4 languages (English, Spanish, Basque, and Swedish), classifier combination performance exceeds the best published results on these data sets. 1

This paper describes an hierarchical approach to WordNet sense distinctions that provides different types of automatic Word Sense Disambiguation (WSD) systems, which perform at varying levels of accuracy. For tasks where fine-grained sense distinctions may not be essential, an accurate coarse-grained WSD system may be sufficient. The paper discusses the criteria behind the three different levels of sense granularity, as well as the machine learning approach used by the WSD system. 1

This paper investigates several augmented mixture models that are competitive alternatives to standard Bayesian models and prove to be very suitable to word sense disambiguation and related classification tasks. We present a new classification correction technique that successfully addresses the problem of under-estimation of infrequent classes in the training data. We show that the mixture models are boosting-friendly and that both Adaboost and our original correction technique can improve the results of the raw model significantly, achieving stateof -the-art performance on several standard test sets in four languages. With substantially different output to Nave Bayes and other statistical methods, the investigated models are also shown to be effective participants in classifier combination.

We describe an approach to the automatic creation of a sense tagged corpus intended to train a word sense disambiguation (WSD) system for English-Portuguese machine translation. The approach uses parallel corpora, translation dictionaries and a set of straightforward heuristics. In an evaluation with nine corpora containing 10 ambiguous verbs, the approach achieved an average precision of 94%, compared with 58% when a state of the art statistical alignment tool was used. The resulting corpus consists of 113,802 instances tagged with the senses (i.e., translations) of the 10 verbs. Besides the word-sense tags, this corpus provides other useful information, such as POS-tags, and can be readily used as input to supervised machine learning algorithms in order to build WSD models for machine translation.