Identification of the most frequent sense of a polysemous word is an important semantic task. We introduce two concepts that can benefit MFS detection: companions, which are the most frequently co-occurring words, and the most frequent translation in a bitext. We present two novel methods that incorporate these new concepts, and show that they advance the state of the art on MFS detection.
@cite_20 present a method for based on a thesaurus constructed from a parsed corpus. This thesaurus is used to induce a word similarity function, which they use to assess the prevalence of each sense of a given target word. They perform both intrinsic and extrinsic evaluations; we compare to their reported results to the extent their experimental setup allows. This method was subsequently applied to a WSD shared task @cite_23 , and to the identification of infrequent word senses @cite_12 . An extended analysis of the method was presented by @cite_5 . @cite_39 adapt this method to Japanese using only the glosses of words, excluding the use of semantic networks such as WordNet.
@cite_39 @cite_23 @cite_5 @cite_20 @cite_12
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This paper explores the large-scale acquisition of sense-tagged examples for Word Sense Disambiguation (WSD). We have applied the “WordNet monosemous relatives” method to construct automatically a web corpus that we have used to train disambiguation systems. The corpus-building process has highlighted important factors, such as the distribution of senses (bias). The corpus has been used to train WSD algorithms that include supervised methods (combining automatic and manuallytagged examples), minimally supervised (requiring sense bias information from hand-tagged corpora), and fully unsupervised. These methods were tested on the Senseval-2 lexical sample test set, and compared successfully to other systems with minimum or no supervision. 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. We describe here a method for automatically identifying word sense variation in a dated collection of historical books in a large digital library. By leveraging a small set of known translation book pairs to induce a bilingual sense inventory and labeled training data for a WSD classifier, we are able to automatically classify the Latin word senses in a 389 million word corpus and track the rise and fall of those senses over a span of two thousand years. We evaluate the performance of seven different classifiers both in a tenfold test on 83,892 words from the aligned parallel corpus and on a smaller, manually annotated sample of 525 words, measuring both the overall accuracy of each system and how well that accuracy correlates (via mean square error) to the observed historical variation. 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. Lexical-semantic resources, including thesauri and WORDNET, have been successfully incorporated into a wide range of applications in Natural Language Processing. However they are very difficult and expensive to create and maintain, and their usefulness has been severely hampered by their limited coverage, bias and inconsistency. Automated and semi-automated methods for developing such resources are therefore crucial for further resource development and improved application performance. Systems that extract thesauri often identify similar words using the distributional hypothesis that similar words appear in similar contexts. This approach involves using corpora to examine the contexts each word appears in and then calculating the similarity between context distributions. Different definitions of context can be used, and I begin by examining how different types of extracted context influence similarity. To be of most benefit these systems must be capable of finding synonyms for rare words. Reliable context counts for rare events can only be extracted from vast collections of text. In this dissertation I describe how to extract contexts from a corpus of over 2 billion words. I describe techniques for processing text on this scale and examine the trade-off between context accuracy, information content and quantity of text analysed. Distributional similarity is at best an approximation to semantic similarity. I develop improved approximations motivated by the intuition that some events in the context distribution are more indicative of meaning than others. For instance, the object-of-verb context wear is far more indicative of a clothing noun than get. However, existing distributional techniques do not effectively utilise this information. The new context-weighted similarity metric I propose in this dissertation significantly outperforms every distributional similarity metric described in the literature. Nearest-neighbour similarity algorithms scale poorly with vocabulary and context vector size. To overcome this problem I introduce a new context-weighted approximation algorithm with bounded complexity in context vector size that significantly reduces the system runtime with only a minor performance penalty. I also describe a parallelized version of the system that runs on a Beowulf cluster for the 2 billion word experiments. To evaluate the context-weighted similarity measure I compare ranked similarity lists against gold-standard resources using precision and recall-based measures from Information Retrieval, We describe a method for automatic word sense disambiguation using a text corpus and a machine-readble dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method can learn even from very sparse training data, achieving over 92 correct disambiguation performance. This paper presents the description and evaluation framework of SemEval-2010 Word Sense Induction & Disambiguation task, as well as the evaluation results of 26 participating systems. In this task, participants were required to induce the senses of 100 target words using a training set, and then disambiguate unseen instances of the same words using the induced senses. Systems' answers were evaluated in: (1) an unsupervised manner by using two clustering evaluation measures, and (2) a supervised manner in a WSD task. An unsupervised iterative approach for extracting a new lexicon (or unknown words) from a Chinese text corpus is proposed in this paper. Instead of using a non-iterative segmentation-merging-filtering-and-disambiguation approach, the proposed method iteratively integrates the contextual constraints (among word candidates) and a joint character association metric to progressively improve the segmentation results of the input corpus (and thus the new word list.) An augmented dictionary, which includes potential unknown words (in addition to known words), is used to segment the input corpus, unlike traditional approaches which use only known words for segmentation. In the segmentation process, the augmented dictionary is used to impose contextual constraints over known words and potential unknown words within input sentences; an unsupervised Viterbi Training process is then applied to ensure that the selected potential unknown words (and known words) maximize the likelihood of the input corpus. On the other hand, the joint character association metric (which reflects the global character association characteristics across the corpus) is derived by integrating several commonly used word association metrics, such as mutual information and entropy, with a joint Gaussian mixture density function; such integration allows the filter to use multiple features simultaneously to evaluate character association, unlike traditional filters which apply multiple features independently. The proposed method then allows the contextual constraints and the joint character association metric to enhance each other; this is achieved by iteratively applying the joint association metric to truncate unlikely unknown words in the augmented dictionary and using the segmentation result to improve the estimation of the joint association metric. The refined augmented dictionary and improved estimation are then used in the next iteration to acquire better segmentation and carry out more reliable filtering. Experiments show that both the precision and recall rates are improved almost monotonically, in contrast to non-iterative segmentation-merging-filtering-and-disambiguation approaches, which often sacrifice precision for recall or vice versa. With a corpus of 311,591 sentences, the performance is 76 (bigram), 54 (trigram), and 70 (quadragram) in F-measure, which is significantly better than using the non-iterative approach with F-measures of 74 (bigram), 46 (trigram), and 58 (quadragram). An acid test for any new Word Sense Disambiguation (WSD) algorithm is its performance against the Most Frequent Sense (MFS). The field of WSD has found the MFS baseline very hard to beat. Clearly, if WSD researchers had access to MFS values, their striving to better this heuristic will push the WSD frontier. However, getting MFS values requires sense annotated corpus in enormous amounts, which is out of bounds for most languages, even if their WordNets are available. In this paper, we propose an unsupervised method for MFS detection from the untagged corpora, which exploits word embeddings. We compare the word embedding of a word with all its sense embeddings and obtain the predominant sense with the highest similarity. We observe significant performance gain for Hindi WSD over the WordNet First Sense (WFS) baseline. As for English, the SemCor baseline is bettered for those words whose frequency is greater than 2. Our approach is language and domain independent. Strictly corpus-based measures of semantic distance conflate co-occurrence information pertaining to the many possible senses of target words. We propose a corpus-thesaurus hybrid method that uses soft constraints to generate word-senseaware distributional profiles (DPs) from coarser "concept DPs" (derived from a Roget-like thesaurus) and sense-unaware traditional word DPs (derived from raw text). Although it uses a knowledge source, the method is not vocabulary-limited: if the target word is not in the thesaurus, the method falls back gracefully on the word's co-occurrence information. This allows the method to access valuable information encoded in a lexical resource, such as a thesaurus, while still being able to effectively handle domain-specific terms and named entities. Experiments on word-pair ranking by semantic distance show the new hybrid method to be superior to others. Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers, the equivalent of "society" is "database," and the equivalent of "use" is "a way to search the database". We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts, we use the World Wide Web (WWW) as the database, and Google as the search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the WWW using Google page counts. The WWW is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87 percent with the expert crafted WordNet categories 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 . We propose the use of the word categories and embeddings induced from raw text as auxiliary features in dependency parsing. To induce word features, we make use of contextual, morphologic and orthographic properties of the words. To exploit the contextual information, we make use of substitute words, the most likely substitutes for target words, generated by using a statistical language model. We generate morphologic and orthographic properties of word types in an unsupervised manner. We use a co-occurrence model with these properties to embed words onto a 25dimensional unit sphere. The AI-KU system shows improvements for some of the languages it is trained on for the first Shared Task of Statistical Parsing of Morphologically Rich Languages. The evaluation of several tasks in lexical semantics is often limited by the lack of large amounts of manual annotations, not only for training purposes, but also for testing purposes. Word Sense Disambiguation (WSD) is a case in point, as hand-labeled datasets are particularly hard and time-consuming to create. Consequently, evaluations tend to be performed on a small scale, which does not allow for in-depth analysis of the factors that determine a systems' performance. In this paper we address this issue by means of a realistic simulation of large-scale evaluation for the WSD task. We do this by providing two main contributions: First, we put forward two novel approaches to the wide-coverage generation of semantically aware pseudowords (i.e., artificial words capable of modeling real polysemous words); second, we leverage the most suitable type of pseudoword to create large pseudosense-annotated corpora, which enable a large-scale experimental framework for the comparison of state-of-the-art supervised and knowledge-based algorithms. Using this framework, we study the impact of supervision and knowledge on the two major disambiguation paradigms and perform an in-depth analysis of the factors which affect their performance. The three corpus-based statistical sense resolution methods studied here attempt to infer the correct sense of a polysemous word by using knowledge about patterns of word cooccurrences. The techniques were based on Bayesian decision theory, neural, networks, and content vectors as used in information retrieval. To understand these methods better, we posed a very specific problem: given a set of contexts, each containing the noun line in a known sense, construct a classifier that selects the correct sense of line for new contexts. To see how the degree of polysemy affects performance, results from three- and six-sense tasks are compared.The results demonstrate that each of the techniques is able to distinguish six senses of line with an accuracy greater than 70 . Furthermore, the response patterns of the classifiers are, for the most part, statistically indistinguishable from one another. Comparison of the two tasks suggests that the degree of difficulty involved in resolving individual senses is a greater performance factor than the degree of polysemy. In this paper we present the Multilingual AllWords Sense Disambiguation and Entity Linking task. Word Sense Disambiguation (WSD) and Entity Linking (EL) are well-known problems in the Natural Language Processing field and both address the lexical ambiguity of language. Their main difference lies in the kind of meaning inventories that are used: EL uses encyclopedic knowledge, while WSD uses lexicographic information. Our aim with this task is to analyze whether, and if so, how, using a resource that integrates both kinds of inventories (i.e., BabelNet 2.5.1) might enable WSD and EL to be solved by means of similar (even, the same) methods. Moreover, we investigate this task in a multilingual setting and for some specific domains. This paper describes our system for Task 11 of SemEval-2013. In the task, participants are provided with a set of ambiguous search queries and the snippets returned by a search engine, and are asked to associate senses with the snippets. The snippets are then clustered using the sense assignments and systems are evaluated based on the quality of the snippet clusters. Our system adopts a preexisting Word Sense Induction (WSI) methodology based on Hierarchical Dirichlet Process (HDP), a non-parametric topic model. Our system is trained over extracts from the full text of English Wikipedia, and is shown to perform well in the shared task. In Semantic Textual Similarity (STS), systems rate the degree of semantic equivalence, on a graded scale from 0 to 5, with 5 being the most similar. This year we set up two tasks: (i) a core task (CORE), and (ii) a typed-similarity task (TYPED). CORE is similar in set up to SemEval STS 2012 task with pairs of sentences from sources related to those of 2012, yet different in genre from the 2012 set, namely, this year we included newswire headlines, machine translation evaluation datasets and multiple lexical resource glossed sets. TYPED, on the other hand, is novel and tries to characterize why two items are deemed similar, using cultural heritage items which are described with metadata such as title, author or description. Several types of similarity have been defined, including similar author, similar time period or similar location. The annotation for both tasks leverages crowdsourcing, with relative high interannotator correlation, ranging from 62 to 87 . The CORE task attracted 34 participants with 89 runs, and the TYPED task attracted 6 teams with 14 runs. The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts.