1,343 research outputs found
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
The success of deep learning methods hinges on the availability of large
training datasets annotated for the task of interest. In contrast to human
intelligence, these methods lack versatility and struggle to learn and adapt
quickly to new tasks, where labeled data is scarce. Meta-learning aims to solve
this problem by training a model on a large number of few-shot tasks, with an
objective to learn new tasks quickly from a small number of examples. In this
paper, we propose a meta-learning framework for few-shot word sense
disambiguation (WSD), where the goal is to learn to disambiguate unseen words
from only a few labeled instances. Meta-learning approaches have so far been
typically tested in an -way, -shot classification setting where each task
has classes with examples per class. Owing to its nature, WSD deviates
from this controlled setup and requires the models to handle a large number of
highly unbalanced classes. We extend several popular meta-learning approaches
to this scenario, and analyze their strengths and weaknesses in this new
challenging setting.Comment: Added additional experiment
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
Word Sense Disambiguation of Corpus of Historical Japanese Using Japanese BERT Trained with Contemporary Texts
application/pdfTokyo University of Agriculture and TechnologyTokyo University of Agriculture and TechnologyNational Institute for Japanese Language and Linguisticshttps://aclanthology.org/2022.paclic-1.49/journal articl
Query Expansion with Locally-Trained Word Embeddings
Continuous space word embeddings have received a great deal of attention in
the natural language processing and machine learning communities for their
ability to model term similarity and other relationships. We study the use of
term relatedness in the context of query expansion for ad hoc information
retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when
trained globally, underperform corpus and query specific embeddings for
retrieval tasks. These results suggest that other tasks benefiting from global
embeddings may also benefit from local embeddings
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