Worst-case complexity and empirical evaluation of artificial intelligence methods for unsupervised word sense disambiguation

Reverse engineering queries from given data, as in the case of query-by-example and query definability, is an important problem with many applications that has recently gained attention in the areas where symbolic artificial intelligence meets learning. In the presence of ontologies this problem was recently studied for Horn-ALC and Horn-ALCI. The main contribution of this paper is to take a first look at the case of DL-Lite, to identify cases where the addition of the ontology does not increase the worst-case complexity of the problem. Unfortunately, reverse engineering conjunctive queries is known to be very hard, even for plain databases, since the smallest witness query is known to be exponential in general. In the light of this, we outline some possible research directions for exploiting the ontology in order to obtain smaller witness queries.

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An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation

10.3115/1118693.1118699 ◽

2002 ◽

Cited By ~ 57

Author(s):

Yoong Keok Lee ◽

Hwee Tou Ng

Keyword(s):

Word Sense Disambiguation ◽

Learning Algorithms ◽

Empirical Evaluation ◽

Word Sense ◽

Knowledge Sources ◽

Sense Disambiguation

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A Composite Kernel for Word Sense Disambiguation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.530-531.522 ◽

2014 ◽

Vol 530-531 ◽

pp. 522-525

Author(s):

Ting Hua Wang ◽

Wen Sheng Zhu ◽

Qiong Zhang ◽

Hai Hui Xie

Keyword(s):

Word Sense Disambiguation ◽

Empirical Evaluation ◽

Ambiguous Word ◽

Kernel Functions ◽

Learning Approaches ◽

Word Sense ◽

Sources Of Information ◽

Composite Kernel ◽

Sense Disambiguation ◽

Kernel Combination

The success of supervised learning approaches to word sensed disambiguation (WSD) is largely dependent on the representation of the context in which an ambiguous word occurs. In practice, different kernel functions can be designed according to different representations since kernels can be well defined on general types of data, such as vectors, sequences, trees, as well as graphs. In this paper, we present a composite kernel, which is a linear combination of two types of kernels, i.e., bag of words (BOW) kernel and sequence kernel, for WSD. The benefit of kernel combination is that it allows to integrate heterogeneous sources of information in a simple and effective way. Empirical evaluation shows that the composite kernel can consistently improve the performance of WSD.

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Generic Preferences over Subsets of Structured Objects

Journal of Artificial Intelligence Research ◽

10.1613/jair.2653 ◽

2009 ◽

Vol 34 ◽

pp. 133-164 ◽

Cited By ~ 3

Author(s):

M. Binshtok ◽

R. I. Brafman ◽

C. Domshlak ◽

S. E. Shiomony

Keyword(s):

Empirical Evaluation ◽

Efficient Manner ◽

Worst Case ◽

Case Complexity ◽

Optimal Subset ◽

Online Newspaper ◽

Worst Case Complexity ◽

Structured Objects ◽

The Individual ◽

Preference Specification

Various tasks in decision making and decision support systems require selecting a preferred subset of a given set of items. Here we focus on problems where the individual items are described using a set of characterizing attributes, and a generic preference specification is required, that is, a specification that can work with an arbitrary set of items. For example, preferences over the content of an online newspaper should have this form: At each viewing, the newspaper contains a subset of the set of articles currently available. Our preference specification over this subset should be provided offline, but we should be able to use it to select a subset of any currently available set of articles, e.g., based on their tags. We present a general approach for lifting formalisms for specifying preferences over objects with multiple attributes into ones that specify preferences over subsets of such objects. We also show how we can compute an optimal subset given such a specification in a relatively efficient manner. We provide an empirical evaluation of the approach as well as some worst-case complexity results.

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