4,616 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
AutoSense Model for Word Sense Induction
Word sense induction (WSI), or the task of automatically discovering multiple
senses or meanings of a word, has three main challenges: domain adaptability,
novel sense detection, and sense granularity flexibility. While current latent
variable models are known to solve the first two challenges, they are not
flexible to different word sense granularities, which differ very much among
words, from aardvark with one sense, to play with over 50 senses. Current
models either require hyperparameter tuning or nonparametric induction of the
number of senses, which we find both to be ineffective. Thus, we aim to
eliminate these requirements and solve the sense granularity problem by
proposing AutoSense, a latent variable model based on two observations: (1)
senses are represented as a distribution over topics, and (2) senses generate
pairings between the target word and its neighboring word. These observations
alleviate the problem by (a) throwing garbage senses and (b) additionally
inducing fine-grained word senses. Results show great improvements over the
state-of-the-art models on popular WSI datasets. We also show that AutoSense is
able to learn the appropriate sense granularity of a word. Finally, we apply
AutoSense to the unsupervised author name disambiguation task where the sense
granularity problem is more evident and show that AutoSense is evidently better
than competing models. We share our data and code here:
https://github.com/rktamplayo/AutoSense.Comment: AAAI 201
Similarity-Based Models of Word Cooccurrence Probabilities
In many applications of natural language processing (NLP) it is necessary to
determine the likelihood of a given word combination. For example, a speech
recognizer may need to determine which of the two word combinations ``eat a
peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine
the likelihood of a word combination from its frequency in a training corpus.
However, the nature of language is such that many word combinations are
infrequent and do not occur in any given corpus. In this work we propose a
method for estimating the probability of such previously unseen word
combinations using available information on ``most similar'' words.
We describe probabilistic word association models based on distributional
word similarity, and apply them to two tasks, language modeling and pseudo-word
disambiguation. In the language modeling task, a similarity-based model is used
to improve probability estimates for unseen bigrams in a back-off language
model. The similarity-based method yields a 20% perplexity improvement in the
prediction of unseen bigrams and statistically significant reductions in
speech-recognition error.
We also compare four similarity-based estimation methods against back-off and
maximum-likelihood estimation methods on a pseudo-word sense disambiguation
task in which we controlled for both unigram and bigram frequency to avoid
giving too much weight to easy-to-disambiguate high-frequency configurations.
The similarity-based methods perform up to 40% better on this particular task.Comment: 26 pages, 5 figure
An effective, low-cost measure of semantic relatedness obtained from Wikipedia links
This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide structured world knowledge about the terms of interest. Out approach is unique in that it does so using the hyperlink structure of Wikipedia rather than its category hierarchy or textual content. Evaluation with manually defined measures of semantic relatedness reveals this to be an effective compromise between the ease of computation of the former approach and the accuracy of the latter
The Epistemological Foundations of Knowledge Representations
This paper looks at the epistemological foundations of knowledge
representations embodied in retrieval languages. It considers questions
such as the validity of knowledge representations and their effectiveness
for the purposes of retrieval and automation. The knowledge
representations it considers are derived from three theories of meaning that
have dominated twentieth-century philosophy.published or submitted for publicatio
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