5,166 research outputs found
The interaction of knowledge sources in word sense disambiguation
Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results.
We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94% on our evaluation corpus.Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems
Disambiguation strategies for cross-language information retrieval
This paper gives an overview of tools and methods for Cross-Language Information Retrieval (CLIR) that are developed within the Twenty-One project. The tools and methods are evaluated with the TREC CLIR task document collection using Dutch queries on the English document base. The main issue addressed here is an evaluation of two approaches to disambiguation. The underlying question is whether a lot of effort should be put in finding the correct translation for each query term before searching, or whether searching with more than one possible translation leads to better results? The experimental study suggests that the quality of search methods is more important than the quality of disambiguation methods. Good retrieval methods are able to disambiguate translated queries implicitly during searching
D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation
Word sense disambiguation (WSD) is a problem in the field of computational
linguistics given as finding the intended sense of a word (or a set of words)
when it is activated within a certain context. WSD was recently addressed as a
combinatorial optimization problem in which the goal is to find a sequence of
senses that maximize the semantic relatedness among the target words. In this
article, a novel algorithm for solving the WSD problem called D-Bees is
proposed which is inspired by bee colony optimization (BCO)where artificial bee
agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a
standard dataset (SemEval 2007 coarse-grained English all-words task corpus)and
is compared to simulated annealing, genetic algorithms, and two ant colony
optimization techniques (ACO). It will be observed that the BCO and ACO
approaches are on par
SupWSD: a flexible toolkit for supervised word sense disambiguation
In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at http://github.com/SI3P/SupWSD
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interactions between words in the context of a sentence. Embeddings and
composition layers are jointly learned against a generic objective that
enhances the vectors with syntactic information from the surrounding context.
Furthermore, each word is associated with a number of senses, the most
plausible of which is selected dynamically during the composition process. We
evaluate the produced vectors qualitatively and quantitatively with positive
results. At the sentence level, the effectiveness of the framework is
demonstrated on the MSRPar task, for which we report results within the
state-of-the-art range.Comment: Accepted for presentation at EMNLP 201
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
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