5,828 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
Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation
Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources
Deriving Verb Predicates By Clustering Verbs with Arguments
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
have proved useful, but have limited coverage. Verb classes automatically
induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
hand, can give clusters with much larger coverage, and can be adapted to
specific corpora such as Twitter. We present a method for clustering the
outputs of VerbKB: verbs with their multiple argument types, e.g.
"marry(person, person)", "feel(person, emotion)." We make use of a novel
low-dimensional embedding of verbs and their arguments to produce high quality
clusters in which the same verb can be in different clusters depending on its
argument type. The resulting verb clusters do a better job than hand-built
clusters of predicting sarcasm, sentiment, and locus of control in tweets
A Crowdsourced Frame Disambiguation Corpus with Ambiguity
We present a resource for the task of FrameNet semantic frame disambiguation
of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations
were collected using a novel crowdsourcing approach with multiple workers per
sentence to capture inter-annotator disagreement. In contrast to the typical
approach of attributing the best single frame to each word, we provide a list
of frames with disagreement-based scores that express the confidence with which
each frame applies to the word. This is based on the idea that inter-annotator
disagreement is at least partly caused by ambiguity that is inherent to the
text and frames. We have found many examples where the semantics of individual
frames overlap sufficiently to make them acceptable alternatives for
interpreting a sentence. We have argued that ignoring this ambiguity creates an
overly arbitrary target for training and evaluating natural language processing
systems - if humans cannot agree, why would we expect the correct answer from a
machine to be any different? To process this data we also utilized an expanded
lemma-set provided by the Framester system, which merges FN with WordNet to
enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs
whose lemmas are not part of FN. Finally we present metrics for evaluating
frame disambiguation systems that account for ambiguity.Comment: Accepted to NAACL-HLT201
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