In order to deal with ambiguity in natural language, it is common to organise words, according to their senses, in synsets, which are groups of synonymous words that can be seen as concepts. The manual creation of a broad-coverage synset base is a time-consuming task, so we take advantage of dictionary definitions for extracting synonymy pairs and clustering for identifying synsets. Since word senses are not discrete, we create fuzzy synsets, where each word has a membership degree. We report on the results of the creation of a fuzzy synset base for Portuguese, from three electronic dictionaries. The resulting resource is larger than existing hancrafted Portuguese thesauri.

Past research has shown that dictionaries and glosses can be beneficial in computer assisted language learning, particularly in vocabulary learning. We propose that L2 vocabulary learners can benefit from the use of a dictionary whose definitions are sensitive to the provided reading context, and that advances in the natural language processing task of word sense disambiguation can be used to automatically order the definitions of such a dictionary. An in-vivo study was conducted with ESL students to investigate the effect that the order of definitions has on vocabulary learning using REAP, a computer based vocabulary tutor. Our results showed that students benefited from having the algorithmically determined best definitions listed at the top of the definition list. Furthermore, our results suggest that word sense disambiguation may currently be good enough for use in intelligent language tutoring environments.

Semantic networks qualify the meaning of an edge relating any two vertices. Determining which vertices are most "central" in a semantic network is difficult because one relationship type may be deemed subjectively more important than another. For this reason, research into semantic network metrics has focused primarily on context-based rankings (i.e. user prescribed contexts). Moreover, many of the current semantic network metrics rank semantic associations (i.e. directed paths between two vertices) and not the vertices themselves. This article presents a framework for calculating semantically meaningful primary eigenvector-based metrics such as eigenvector centrality and PageRank in semantic networks using a modified version of the random walker model of Markov chain analysis. Random walkers, in the context of this article, are constrained by a grammar, where the grammar is a user defined data structure that determines the meaning of the final vertex ranking. The ideas in this article are presented within the context of the Resource Description Framework (RDF) of the Semantic Web initiative.

This article presents some fundamental ideas about representing knowledge and dealing with meaning in computer representations. I will describe the issues as I currently understand them and describe how they came about, how they fit together, what problems they solve, and some of the things that the resulting framework can do. The ideas apply not just to graph-structured "node-and-link" representations, sometimes called semantic networks, but also to representations referred to variously as frames with slots, entities with relationships, objects with attributes, tables with columns, and records with fields and to the classes and variables of object-oriented data structures. I will start by describing some background experiences and thoughts that preceded the writing of my 1975 paper, "What's in a Link," which introduced many of these issues. After that, I will present some of the key ideas from that paper with a discussion of how some of those ideas have matured since then. Finally, I will describe some practical applications of these ideas in the context of knowledge access and information retrieval and will conclude with some thoughts about where I think we can go from here.

In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds--- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations.

Access to restricted content on Oxford Academic is often provided through institutional subscriptions and purchases. Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account. Choose this option to get remote access when outside your institution. Shibboleth / Open Athens technology is used to provide single sign-on between your institution's website and Oxford Academic.

William Heinemann Ltd., London

This paper examines in detail the history of a set of network-structured formalisms for knowledge representation - the so-called semantic networks. While these nets have for the most part retained their basic associative nature, their primitive representational elements have differed significantly from one project to the next. These differences in underlying primitives are symptomatic of deeper philosophical disparities, and a set of five significantly different levels at which networks can be understood are discussed. One of these levels, the epistemological, or knowledge-structuring, level, has played an important implicit part in all previous notations, and is here made explicit in a way that allows a new type of network formalism to be specified. This new type of formalism accounts precisely for operations like individuation of description, internal concept structure in terms of roles and interrelations between them, and structured inheritance.

W'e present a formal syntax and semantics for the SNePS Semantic Network P recessing System (Shapiro 1979), based on a \leinongian theory of the intensional objects of thought (Rapaport 198Sa). Such a theory avoids possible worlds and is appropriate t or AI considered as "computational philosophy"-AI as the study of how intelligence is possible-or "computational psychology"- .ql

Semantic networks constitute one of the many attempts to capture human knowledge in an abstraction suitable for processing by computer program. While semantic nets enjoy widespread popularity, they seem never to live up to their authors' expectations of expressive power and ease of construction. This paper examines the fundamentals of network notation, in order to understand why the “formalism” has not been the panacea it was once hoped to be. We focus here on “concepts”—what net-authors think they are, and how network nodes might represent them. The simplistic view of concept nodes as representing extensional sets is examined, and found wanting in several respects.