2,276 research outputs found
Enriching very large ontologies using the WWW
This paper explores the possibility to exploit text on the world wide web in
order to enrich the concepts in existing ontologies. First, a method to
retrieve documents from the WWW related to a concept is described. These
document collections are used 1) to construct topic signatures (lists of
topically related words) for each concept in WordNet, and 2) to build
hierarchical clusters of the concepts (the word senses) that lexicalize a given
word. The overall goal is to overcome two shortcomings of WordNet: the lack of
topical links among concepts, and the proliferation of senses. Topic signatures
are validated on a word sense disambiguation task with good results, which are
improved when the hierarchical clusters are used.Comment: 6 page
A Word Sense-Oriented User Interface for Interactive Multilingual Text Retrieval
In this paper we present an interface for supporting a user in an interactive cross-language search process using semantic classes. In order to enable users to access multilingual information, different problems have to be solved: disambiguating and translating the query words, as well as categorizing and presenting the results appropriately. Therefore, we first give a brief introduction to word sense disambiguation, cross-language text retrieval and document categorization and finally describe recent achievements of our research towards an interactive multilingual retrieval system. We focus especially on the problem of browsing and navigation of the different word senses in one source and possibly several target languages. In the last part of the paper, we discuss the developed user interface and its functionalities in more detail
Normalized Web Distance and Word Similarity
There is a great deal of work in cognitive psychology, linguistics, and
computer science, about using word (or phrase) frequencies in context in text
corpora to develop measures for word similarity or word association, going back
to at least the 1960s. The goal of this chapter is to introduce the
normalizedis a general way to tap the amorphous low-grade knowledge available
for free on the Internet, typed in by local users aiming at personal
gratification of diverse objectives, and yet globally achieving what is
effectively the largest semantic electronic database in the world. Moreover,
this database is available for all by using any search engine that can return
aggregate page-count estimates for a large range of search-queries. In the
paper introducing the NWD it was called `normalized Google distance (NGD),' but
since Google doesn't allow computer searches anymore, we opt for the more
neutral and descriptive NWD. web distance (NWD) method to determine similarity
between words and phrases. ItComment: Latex, 20 pages, 7 figures, to appear in: Handbook of Natural
Language Processing, Second Edition, Nitin Indurkhya and Fred J. Damerau
Eds., CRC Press, Taylor and Francis Group, Boca Raton, FL, 2010, ISBN
978-142008592
LO-MATCH: A semantic platform for matching migrants' competences with labour market's needs
Citizens' mobility and employability are receiving ever more attention by the European legislation. Various instruments have been defined to overcome lexical and semantic differences in the descriptions of qualifications, résumés and job profiles. However, the above differences still represent a significant constraint when abilities of non-European people have to be validated either for education and training or occupation purposes. In this work, a web platform that exploits semantic technologies to address such heterogeneity issues is presented. The platform allows migrants to annotate their knowledge, skills and competences in a shared format based on the European tools. The resulting knowledge base is then used to enable the automatic matchmaking of job seekers' abilities with companies' needs. The platform can additionally be used to support students and workers in the identification of their competence gap with respect to a given education or occupation opportunity, so that to personalize their further trainin
Similarity of Semantic Relations
There are at least two kinds of similarity. Relational similarity is
correspondence between relations, in contrast with attributional similarity,
which is correspondence between attributes. When two words have a high
degree of attributional similarity, we call them synonyms. When two pairs
of words have a high degree of relational similarity, we say that their
relations are analogous. For example, the word pair mason:stone is analogous
to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA),
a method for measuring relational similarity. LRA has potential applications in many
areas, including information extraction, word sense disambiguation,
and information retrieval. Recently the Vector Space Model (VSM) of information
retrieval has been adapted to measuring relational similarity,
achieving a score of 47% on a collection of 374 college-level multiple-choice
word analogy questions. In the VSM approach, the relation between a pair of words is
characterized by a vector of frequencies of predefined patterns in a large corpus.
LRA extends the VSM approach in three ways: (1) the patterns are derived automatically
from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency
data, and (3) automatically generated synonyms are used to explore variations of the
word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the
average human score of 57%. On the related problem of classifying semantic relations, LRA
achieves similar gains over the VSM
PowerAqua: fishing the semantic web
The Semantic Web (SW) offers an opportunity to develop novel, sophisticated forms of question answering (QA). Specifically, the availability of distributed semantic markup on a large scale opens the way to QA systems which can make use of such semantic information to provide precise, formally derived answers to questions. At the same time the distributed, heterogeneous, large-scale nature of the semantic information introduces significant challenges. In this paper we describe the design of a QA system, PowerAqua, designed to exploit semantic markup on the web to provide answers to questions posed in natural language. PowerAqua does not assume that the user has any prior information about the semantic resources. The system takes as input a natural language query, translates it into a set of logical queries, which are then answered by consulting and aggregating information derived from multiple heterogeneous semantic sources
Learning Analogies and Semantic Relations
We present an algorithm for learning from unlabeled text, based on the
Vector Space Model (VSM) of information retrieval, that can solve verbal
analogy questions of the kind found in the Scholastic Aptitude Test (SAT).
A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D";
for example, mason:stone::carpenter:wood. SAT analogy questions provide
a word pair, A:B, and the problem is to select the most analogous word
pair, C:D, from a set of five choices. The VSM algorithm correctly
answers 47% of a collection of 374 college-level analogy questions
(random guessing would yield 20% correct). We motivate this research by
relating it to work in cognitive science and linguistics, and by applying
it to a difficult problem in natural language processing, determining
semantic relations in noun-modifier pairs. The problem is to classify a
noun-modifier pair, such as "laser printer", according to the semantic
relation between the noun (printer) and the modifier (laser). We use a
supervised nearest-neighbour algorithm that assigns a class to a given
noun-modifier pair by finding the most analogous noun-modifier pair in
the training data. With 30 classes of semantic relations, on a collection
of 600 labeled noun-modifier pairs, the learning algorithm attains an F
value of 26.5% (random guessing: 3.3%). With 5 classes of semantic
relations, the F value is 43.2% (random: 20%). The performance is
state-of-the-art for these challenging problems
Semantics-based selection of everyday concepts in visual lifelogging
Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach
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