Word Sense Disambiguation System Research Articles

Unsupervised Acquisition of Predominant Word Senses

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.

503 POSTER Comparative analysis of microarray testing and immunohistochemistry in patients with carcinoma of unknown primary – CUP syndrome

In this article, we concentrate in conceptual relations as a source of information for Word Sense Disambiguation (WSD) systems. We start with a review the most relevant research in the field, then we implement our own algorithm. As a starting point we have chosen the conceptual density algorithm of Agirre and Rigau. We generalize the original algorithm, parameterizing many aspects. This new algorithm obtains a relative improvement of 24% in terms of precision and recall. We also offer comparative evaluation of our system with respect to the participants in the SENSEVAL-2 disambiguation competition.We conclude that conceptual relations provide a source of information that is insufficient by itself to achieve good disambiguation results, but can, however, be a very accurate heuristic in a combined system.

Word Sense Disambiguation by Combining Classifiers with an Adaptive Selection of Context Representation

Word Sense Disambiguation (WSD) is the task of choosing the right sense of a polysemous word given a context. It is obviously essential for many natural language processing applications such as human-computer communication, machine translation, and information retrieval. In recent years, much attention have been paid to improve the performance of WSD systems by using combination of classifiers. In (Kittler, Hatef, Duin, and Matas 1998), six combination rules including product, sum, max, min, median, and majority voting were derived with a number of strong assumptions, that are unrealistic in many situations and especially in text-related applications. This paper considers a framework of combination strategies based on different representations of context in WSD resulting in these combination rules as well, but without the unrealistic assumptions mentioned above. The experiment was done on four words interest, line, hard, serve; on the DSO dataset it showed high accuracies with median and min combination rules.

Making fine-grained and coarse-grained sense distinctions, both manually and automatically

In this paper we discuss a persistent problem arising from polysemy: namely the difficulty of finding consistent criteria for making fine-grained sense distinctions, either manually or automatically. We investigate sources of human annotator disagreements stemming from the tagging for the English Verb Lexical Sample Task in the SENSEVAL-2 exercise in automatic Word Sense Disambiguation. We also examine errors made by a high-performing maximum entropy Word Sense Disambiguation system we developed. Both sets of errors are at least partially reconciled by a more coarse-grained view of the senses, and we present the groupings we use for quantitative coarse-grained evaluation as well as the process by which they were created. We compare the system's performance with our human annotator performance in light of both fine-grained and coarse-grained sense distinctions and show that well-defined sense groups can be of value in improving word sense disambiguation by both humans and machines.

A Word Sense Disambiguation System Using Modified Naive Bayesian Algorithms for Indonesian Language

A Word Sense Disambiguation System Using Modified Naive Bayesian Algorithms for Indonesian Language

A detailed comparison of WSD systems: an analysis of the system answers for the SENSEVAL-2 English all words task

We compare the word sense disambiguation systems submitted for the English-all-words task in SENSEVAL-2. We give several performance measures for the systems, and analyze correlations between system performance and word features. A decision tree learning algorithm is employed to discover the situations in which systems perform particularly well, and the resulting decision tree is examined. We investigate using a decision tree based on the SENSEVAL systems to (i) filter out senses unlikely to be correct, and to (ii) combine WSD systems. Some combinations created in this way outperform the best SENSEVAL system.

Structural semantic interconnections: a knowledge-based approach to word sense disambiguation

Word Sense Disambiguation (WSD) is traditionally considered an Al-hard problem. A break-through in this field would have a significant impact on many relevant Web-based applications, such as Web information retrieval, improved access to Web services, information extraction, etc. Early approaches to WSD, based on knowledge representation techniques, have been replaced in the past few years by more robust machine learning and statistical techniques. The results of recent comparative evaluations of WSD systems, however, show that these methods have inherent limitations. On the other hand, the increasing availability of large-scale, rich lexical knowledge resources seems to provide new challenges to knowledge-based approaches. In this paper, we present a method, called structural semantic interconnections (SSI), which creates structural specifications of the possible senses for each word in a context and selects the best hypothesis according to a grammar G, describing relations between sense specifications. Sense specifications are created from several available lexical resources that we integrated in part manually, in part with the help of automatic procedures. The SSI algorithm has been applied to different semantic disambiguation problems, like automatic ontology population, disambiguation of sentences in generic texts, disambiguation of words in glossary definitions. Evaluation experiments have been performed on specific knowledge domains (e.g., tourism, computer networks, enterprise interoperability), as well as on standard disambiguation test sets.

Búsqueda de Colocaciones en la Web para Sinónimos de Wordnet

La Web es sin lugar a dudas el repositorio de información más grande jamás construido por el ser humano. Con más de cuatro mil millones de páginas indexadas por los motores de búsqueda públicos, la Web representa el mayor y más amplio corpus textual disponible en la actualidad. Por su valor lingüístico, dado que contiene información en más de 1 500 lenguajes, este corpus está siendo usado con gran éxito en muchas tareas de procesamientodel lenguaje natural. En particular, varios métodos de minería de datos se han aplicado para extraer de la Web algunos tipos de patrones lingüísticos útiles para tareas como la traducción automática y búsqueda de respuestas. En este artículo presentamos un método que permite encontrar combinaciones de palabras significativas a los diferentes sentidos atribuibles a una palabra polisémica. Los experimentos realizados, aunque preliminares, muestran el gran potencial del método propuesto para encontrar estas colocaciones por sentido usando la Web como corpus, así como la viabilidad de la incorporación de dichas colocaciones en sistemas de desambiguación del sentido de las palabras, que pueden a su vez ser usados en sistemas de traducción automática y recuperación de información.

A Word Sense Disambiguation System Using Modified Naive Bayesian Algorithms for Indonesian Language

A Word Sense Disambiguation System Using Modified Naive Bayesian Algorithms for Indonesian Language

Probabilistic word sense disambiguation

We present a theoretically motivated method for creating probabilistic word sense disambiguation (WSD) systems. The method works by composing multiple probabilistic components: such modularity is made possible by an application of Bayesian statistics and Lidstone's smoothing method. We show that a probabilistic WSD system created along these lines is a strong competitor to state-of-the-art WSD systems.

Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences

Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of disambiguating information. We evaluate WSD using selectional preferences acquired for English adjective—noun, subject, and direct object grammatical relationships with respect to a standard test corpus. The selectional preferences are specific to verb or adjective classes, rather than individual word forms, so they can be used to disambiguate the co-occurring adjectives and verbs, rather than just the nominal argument heads. We also investigate use of the one-senseper-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage. Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage. In addition to quantifying performance, we analyze the results to investigate the situations in which the selectional preferences achieve the best precision and in which the one-sense-per-discourse heuristic increases performance.

SENSEVAL, a computer-based approximation to meaning

In this paper we present an overview of the international exercise SENSEVAL, the basic purpose of which is to organize and run evaluation of Word Sense Disambiguation (WSD) algorithms and systems with respect to different words and languages. In order to do this, we first introduce the main goals and the organization of the SENSEVAL exercise; we follow with the methodology developed, the tasks and the linguistic resources created (corpora and dictionaries); and we finally end with some general reflections about the effects on Linguistics and, concretely, on Lexicography motivated by this methodology. We will base on the resources developed for the Spanish SENSEVAL-2 in order to illustrate this methodology.

Parameter optimization for machine-learning of word sense disambiguation

Various Machine Learning (ML) approaches have been demonstrated to produce relatively successful Word Sense Disambiguation (WSD) systems. There are still unexplained differences among the performance measurements of different algorithms, hence it is warranted to deepen the investigation into which algorithm has the right ‘bias’ for this task. In this paper, we show that this is not easy to accomplish, due to intricate interactions between information sources, parameter settings, and properties of the training data. We investigate the impact of parameter optimization on generalization accuracy in a memory-based learning approach to English and Dutch WSD. A ‘word-expert’ architecture was adopted, yielding a set of classifiers, each specialized in one single wordform. The experts consist of multiple memory-based learning classifiers, each taking different information sources as input, combined in a voting scheme. We optimized the architectural and parametric settings for each individual word-expert by performing cross-validation experiments on the learning material. The results of these experiments show that the variation of both the algorithmic parameters and the information sources available to the classifiers leads to large fluctuations in accuracy. We demonstrate that optimization per word-expert leads to an overall significant improvement in the generalization accuracies of the produced WSD systems.

Introduction to the special issue on evaluating word sense disambiguation systems

Has system performance on Word Sense Disambiguation (WSD) reached a limit? Automatic systems don't perform nearly as well as humans on the task, and from the results of the SENSEVAL exercises, recent improvements in system performance appear negligible or even negative. Still, systems do perform much better than the baselines, so something is being done right. System evaluation is crucial to explain these results and to show the way forward. Indeed, the success of any project in WSD is tied to the evaluation methodology used, and especially to the formalization of the task that the systems perform. The evaluation of WSD has turned out to be as difficult as designing the systems in the first place.

Semantic Encoding of Electronic Documents

This paper presents an unsupervised, all-words, word sense disambiguation system for English. The system associates a word with its meaning in a given context using an electronic dictionary as a tagged corpora in order to extract semantic disambiguation rules. The methodology attempts to avoid the data acquisition bottleneck observed in word sense disambiguation techniques. Semantic rules are used as input of a semantic application program encoding a linguistic strategy in order to select the best rule to apply. The semantic rule extraction process as well as the application program is described. The methodology is developed in a client/server architecture, which enables the treatment of large corpora. The evaluation of the system is then detailed and some possible extensions and perspectives are finally proposed.

Senseval/Romanseval: The Framework for Italian

In this paper we present some observations concerning an experiment of (manual/ automatic) semantic tagging of a small Italian corpus performed within the framework of the SENSEVAL/ROMANSEVAL initiative. The main goal of the initiative was to set up a framework for evaluation of Word Sense Disambiguation systems (WSDS) through the comparative analysis of their performance on the same type of data. In this experiment there are two aspects which are of relevance: first, the preparation of the reference annotated corpus, and, second, the evaluation of the systems against it. In both aspects we are mainly interested here in the analysis of the linguistic side which can lead to a better understanding of the problem of semantic annotation of a corpus, be it manual or automatic annotation. In particular, we will investigate, firstly, the reasons for disagreement between human annotators, secondly, some linguistically relevant aspects of the performance of the Italian WSDS and, finally, the lessons learned from the present experiment.

Large scale WSD using learning applied to SENSEVAL

A word sense disambiguation system which is going to be used as part of a NLP system needs to be large scale, able to be optimised towards a specific task and above all accurate. This paper describes the knowledge sources used in a disambiguation system able to achieve all three of these criteria. It is a hybrid system combining sub-symbolic, stochastic and rule-based learning. The paper reports the results achieved in Senseval and analyses them to show the system's strengths and weaknesses relative to other similar systems.

ROMANSEVAL: Results for Italian by SENSE

The paper describes SENSE, a word sense disambiguation system that makes use of different types of cues to infer the most likely sense of a word given its context. Architecture and functioning of the system are briefly illustrated. Results are given for the ROMANSEVAL Italian test corpus of verbs.

SENSE: an analogy-based Word Sense Disambiguation system

The paper describes SENSE, a word sense disambiguation system which makes use of multidimensional analogy-based proportions to infer the most likely sense of a word given its context. Architecture and functioning of the system are illustrated in detail. Results of different experimental settings are given, showing that the system, in spite its conservative bias, successfully copes with the problem of training data sparseness.

Selective sampling for example-based word sense disambiguation

This paper proposes an efficient example sampling method for example-based word sense disambiguation systems. To construct a database of practical size, a considerable overhead for manual sense dis...