882 research outputs found
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
In this paper, we present a novel unsupervised algorithm for word sense
disambiguation (WSD) at the document level. Our algorithm is inspired by a
widely-used approach in the field of genetics for whole genome sequencing,
known as the Shotgun sequencing technique. The proposed WSD algorithm is based
on three main steps. First, a brute-force WSD algorithm is applied to short
context windows (up to 10 words) selected from the document in order to
generate a short list of likely sense configurations for each window. In the
second step, these local sense configurations are assembled into longer
composite configurations based on suffix and prefix matching. The resulted
configurations are ranked by their length, and the sense of each word is chosen
based on a voting scheme that considers only the top k configurations in which
the word appears. We compare our algorithm with other state-of-the-art
unsupervised WSD algorithms and demonstrate better performance, sometimes by a
very large margin. We also show that our algorithm can yield better performance
than the Most Common Sense (MCS) baseline on one data set. Moreover, our
algorithm has a very small number of parameters, is robust to parameter tuning,
and, unlike other bio-inspired methods, it gives a deterministic solution (it
does not involve random choices).Comment: In Proceedings of EACL 201
Selective Sampling for Example-based Word Sense Disambiguation
This paper proposes an efficient example sampling method for example-based
word sense disambiguation systems. To construct a database of practical size, a
considerable overhead for manual sense disambiguation (overhead for
supervision) is required. In addition, the time complexity of searching a
large-sized database poses a considerable problem (overhead for search). To
counter these problems, our method selectively samples a smaller-sized
effective subset from a given example set for use in word sense disambiguation.
Our method is characterized by the reliance on the notion of training utility:
the degree to which each example is informative for future example sampling
when used for the training of the system. The system progressively collects
examples by selecting those with greatest utility. The paper reports the
effectiveness of our method through experiments on about one thousand
sentences. Compared to experiments with other example sampling methods, our
method reduced both the overhead for supervision and the overhead for search,
without the degeneration of the performance of the system.Comment: 25 pages, 14 Postscript figure
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