This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word to a known sense definition based solely on the values of automatically identifiable features in text. These methods and feature sets are found to be more successful in disambiguating nouns rather than adjectives or verbs. Overall, the most accurate of these procedures is McQuitty's similarity analysis in combination with a high dimensional feature set.
While @cite_23 does not discuss distinguishing more than 2 senses of a word, there is no immediate reason to doubt that the one sense per collocation'' rule @cite_24 would still hold for a larger number of senses. In future work we will evaluate using the one sense per collocation'' rule to seed our various methods. This may help in dealing with very skewed distributions of senses since we currently select collocations based simply on frequency.
@cite_24 @cite_23
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Previous work [Gale, Church and Yarowsky, 1992] showed that with high probability a polysemous word has one sense per discourse. In this paper we show that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation. We test this empirical hypothesis for several definitions of sense and collocation, and discover that it holds with 90--99 accuracy for binary ambiguities. We utilize this property in a disambiguation algorithm that achieves precision of 92 using combined models of very local context. A consideration of problems engendered by the use of concordances to study additional word senses. The use of factor analysis as a research tool in lexicography is discussed. It is shown that this method provides information not obtainable through other approaches. This includes provision of several major senses for each word, an indication of the relationship between collocational patterns, & a more detailed analysis of the senses themselves. Sample factor analyses for the collocates of certain & right are presented & discussed. 3 Tables, 11 References. B. Annesser Murray This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints---that words tend to have one sense per discourse and one sense per collocation---exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96 . Abstract Objective The aim of this study was to investigate relations among different aspects in supervised word sense disambiguation (WSD; supervised machine learning for disambiguating the sense of a term in a context) and compare supervised WSD in the biomedical domain with that in the general English domain. Methods The study involves three data sets (a biomedical abbreviation data set, a general biomedical term data set, and a general English data set). The authors implemented three machine-learning algorithms, including (1) naive Bayes (NBL) and decision lists (TDLL), (2) their adaptation of decision lists (ODLL), and (3) their mixed supervised learning (MSL). There were six feature representations (various combinations of collocations, bag of words, oriented bag of words, etc.) and five window sizes (2, 4, 6, 8, and 10). Results Supervised WSD is suitable only when there are enough sense-tagged instances with at least a few dozens of instances for each sense. Collocations combined with neighboring words are appropriate selections for the context. For terms with unrelated biomedical senses, a large window size such as the whole paragraph should be used, while for general English words a moderate window size between 4 and 10 should be used. The performance of the authors' implementation of decision list classifiers for abbreviations was better than that of traditional decision list classifiers. However, the opposite held for the other two sets. Also, the authors' mixed supervised learning was stable and generally better than others for all sets. Conclusion From this study, it was found that different aspects of supervised WSD depend on each other. The experiment method presented in the study can be used to select the best supervised WSD classifier for each ambiguous term. In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation. We tested our WSD program, named LEXAS, on both a common data set used in previous work, as well as on a large sense-tagged corpus that we separately constructed. LEXAS achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of WORDNET. In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation. We tested our WSD program, named Lexas , on both a common data set used in previous work, as well as on a large sense-tagged corpus that we separately constructed. Lexas achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of WordNet . Word sense annotation is a challenging task where annotators distinguish which meaning of a word is present in a given context. In some contexts, a word usage may elicit multiple interpretations, resulting either in annotators disagreeing or in allowing the usage to be annotated with multiple senses. While some works have allowed the latter, the extent to which multiple sense annotations are needed has not been assessed. The present work analyzes a dataset of instances annotated with multiple WordNet senses to assess the causes of the multiple interpretations and their relative frequencies, along with the effect of the multiple senses on the contextual interpretation. We show that contextual underspecification is the primary cause of multiple interpretations but that syllepsis still accounts for more than a third of the cases. In addition, we show that sense coarsening can only partially remove the need for labeling instances with multiple senses and we provide suggestions for how future sense annotation guidelines might be developed to account for this need. In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of hand-tagged data. Whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. We present work on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically. The acquired predominant senses give a precision of 64 on the nouns of the SENSEVAL-2 English all-words task. This is a very promising result given that our method does not require any hand-tagged text, such as SemCor. Furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domain-specific corpora. Unsupervised word sense disambiguation (WSD) methods are an attractive approach to all-words WSD due to their non-reliance on expensive annotated data. Unsupervised estimates of sense frequency have been shown to be very useful for WSD due to the skewed nature of word sense distributions. This paper presents a fully unsupervised topic modelling-based approach to sense frequency estimation, which is highly portable to different corpora and sense inventories, in being applicable to any part of speech, and not requiring a hierarchical sense inventory, parsing or parallel text. We demonstrate the effectiveness of the method over the tasks of predominant sense learning and sense distribution acquisition, and also the novel tasks of detecting senses which aren’t attested in the corpus, and identifying novel senses in the corpus which aren’t captured in the sense inventory. Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications. There has been a great deal of recent research into word sense disambiguation, particularly since the inception of the Senseval evaluation exercises. Because a word often has more than one meaning, resolving word sense ambiguity could benefit applications that need some level of semantic interpretation of language input. A major problem is that the accuracy of word sense disambiguation systems is strongly dependent on the quantity of manually sense-tagged data available, and even the best systems, when tagging every word token in a document, perform little better than a simple heuristic that guesses the first, or predominant, sense of a word in all contexts. The success of this heuristic is due to the skewed nature of word sense distributions. Data for the heuristic can come from either dictionaries or a sample of sense-tagged data. However, there is a limited supply of the latter, and the sense distributions and predominant sense of a word can depend on the domain or source of a document. (The first sense of “star” for example would be different in the popular press and scientific journals). In this article, we expand on a previously proposed method for determining the predominant sense of a word automatically from raw text. We look at a number of different data sources and parameterizations of the method, using evaluation results and error analyses to identify where the method performs well and also where it does not. In particular, we find that the method does not work as well for verbs and adverbs as nouns and adjectives, but produces more accurate predominant sense information than the widely used SemCor corpus for nouns with low coverage in that corpus. We further show that the method is able to adapt successfully to domains when using domain specific corpora as input and where the input can either be hand-labeled for domain or automatically classified. Automatically generating challenging distractors for multiple-choice gap-fill items is still an unsolved problem. We propose to employ context-sensitive lexical inference rules in order to generate distractors that are semantically similar to the gap target word in some sense, but not in the particular sense induced by the gap-fill context. We hypothesize that such distractors should be particularly hard to distinguish from the correct answer. We focus on verbs as they are especially difficult to master for language learners and find that our approach is quite effective. In our test set of 20 items, our proposed method decreases the number of invalid distractors in 90 of the cases, and fully eliminates all of them in 65 . Further analysis on that dataset does not support our hypothesis regarding item difficulty as measured by average error rate of language learners. We conjecture that this may be due to limitations in our evaluation setting, which we plan to address in future work. In this paper we show that an unsupervised method for ranking word senses automatically can be used to identify infrequently occurring senses. We demonstrate this using a ranking of noun senses derived from the BNC and evaluating on the sense-tagged text available in both SemCor and the SENSEVAL-2 English all-words task. We show that the method does well at identifying senses that do not occur in a corpus, and that those that are erroneously filtered but do occur typically have a lower frequency than the other senses. This method should be useful for word sense disambiguation systems, allowing effort to be concentrated on more frequent senses; it may also be useful for other tasks such as lexical acquisition. Whilst the results on balanced corpora are promising, our chief motivation for the method is for application to domain specific text. For text within a particular domain many senses from a generic inventory will be rare, and possibly redundant. Since a large domain specific corpus of sense annotated data is not available, we evaluate our method on domain-specific corpora and demonstrate that sense types identified for removal are predominantly senses from outside the domain. This paper introduces the Word Sketch: a collocation-based resource of proven value for English lexicography. Issues involving the automatic extraction and presentation of salient collocations are discussed. It is further shown how the combination of signicant patterns may lead to even greater precision in the identication of collocations. Existing literature on distributed development in software engineering, and other fields discusses various challenges, including cultural barriers, expertise transfer difficulties, and communication and coordination overhead. Conventional wisdom, in fact, holds that distributed software development is riskier and more challenging than collocated development. We revisit this belief, empirically studying the overall development of Windows Vista and comparing the post-release failures of components that were developed in a distributed fashion with those that were developed by collocated teams. We found a negligible difference in failures. This difference becomes even less significant when controlling for the number of developers working on a binary. Furthermore, we also found that component characteristics (such as code chum, complexity, dependency information, and test code cover- age) differ very little between distributed and collocated components. Finally, we examine the software process used during the Vista development cycle and examine how it may have mitigated some of the difficulties of distributed development introduced in prior work in this area. In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The first (or predominant) sense heuristic assumes the availability of handtagged data. Whilst there are hand-tagged corpora available for some languages, these are relatively small in size and many word forms either do not occur, or occur infrequently. In this paper we investigate the performance of an unsupervised first sense heuristic where predominant senses are acquired automatically from raw text. We evaluate on both the SENSEVAL-2 and SENSEVAL-3 English allwords data. For accurate WSD the first sense heuristic should be used only as a back-off, where the evidence from the context is not strong enough. In this paper however, we examine the performance of the automatically acquired first sense in isolation since it turned out that the first sense taken from SemCor outperformed many systems in SENSEVAL-2. This paper introduces a simple and very general theory of compressive sensing. In this theory, the sensing mechanism simply selects sensing vectors independently at random from a probability distribution F; it includes all standard models-e.g., Gaussian, frequency measurements-discussed in the literature, but also provides a framework for new measurement strategies as well. We prove that if the probability distribution F obeys a simple incoherence property and an isotropy property, one can faithfully recover approximately sparse signals from a minimal number of noisy measurements. The novelty is that our recovery results do not require the restricted isometry property (RIP) to hold near the sparsity level in question, nor a random model for the signal. As an example, the paper shows that a signal with s nonzero entries can be faithfully recovered from about s logn Fourier coefficients that are contaminated with noise. We participated in the SemEval-2007 coarse-grained English all-words task and fine-grained English all-words task. We used a supervised learning approach with SVM as the learning algorithm. The knowledge sources used include local collocations, parts-of-speech, and surrounding words. We gathered training examples from English-Chinese parallel corpora, SemCor, and DSO corpus. While the fine-grained sense inventory of WordNet was used to train our system employed for the fine-grained English all-words task, our system employed for the coarse-grained English all-words task was trained with the coarse-grained sense inventory released by the task organizers. Our scores (for both recall and precision) are 0.825 and 0.587 for the coarse-grained English all-words task and fine-grained English all-words task respectively. These scores put our systems in the first place for the coarse-grained English all-words task and the second place for the fine-grained English all-words task. Distributions of the senses of words are often highly skewed. This fact is exploited by word sense disambiguation (WSD) systems which back off to the predominant sense of a word when contextual clues are not strong enough. The domain of a document has a strong influence on the sense distribution of words, but it is not feasible to produce large manually annotated corpora for every domain of interest. In this paper we describe the construction of three sense annotated corpora in different domains for a sample of English words. We apply an existing method for acquiring predominant sense information automatically from raw text, and for our sample demonstrate that (1) acquiring such information automatically from a mixed-domain corpus is more accurate than deriving it from SemCor, and (2) acquiring it automatically from text in the same domain as the target domain performs best by a large margin. We also show that for an all words WSD task this automatic method is best focussed on words that are salient to the domain, and on words with a different acquired predominant sense in that domain compared to that acquired from a balanced corpus. While workload collocation is a necessity to increase energy efficiency of contemporary multi-core hardware, it also increases the risk of performance anomalies due to workload interference. Pinning certain workloads to a subset of CPUs is a simple approach to increasing workload isolation, but its effect depends on workload type and system architecture. Apart from common sense guidelines, the effect of pinning has not been extensively studied so far. In this paper we study the impact of CPU pinning on performance interference and energy efficiency for pairs of collocated workloads. Besides various combinations of workloads, virtualization and resource isolation, we explore the effects of pinning depending on the level of background load. The presented results are based on more than 1000 experiments carried out on an Intel-based NUMA system, with all power management features enabled to reflect real-world settings. We find that less common CPU pinning configurations improve energy efficiency at partial background loads, indicating that systems hosting collocated workloads could benefit from dynamic CPU pinning based on CPU load and workload type.