4,912 research outputs found
Embeddings for word sense disambiguation: an evaluation study
Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-of-the-art WSD system that incorporates several standard WSD features
Do Multi-Sense Embeddings Improve Natural Language Understanding?
Learning a distinct representation for each sense of an ambiguous word could
lead to more powerful and fine-grained models of vector-space representations.
Yet while `multi-sense' methods have been proposed and tested on artificial
word-similarity tasks, we don't know if they improve real natural language
understanding tasks. In this paper we introduce a multi-sense embedding model
based on Chinese Restaurant Processes that achieves state of the art
performance on matching human word similarity judgments, and propose a
pipelined architecture for incorporating multi-sense embeddings into language
understanding.
We then test the performance of our model on part-of-speech tagging, named
entity recognition, sentiment analysis, semantic relation identification and
semantic relatedness, controlling for embedding dimensionality. We find that
multi-sense embeddings do improve performance on some tasks (part-of-speech
tagging, semantic relation identification, semantic relatedness) but not on
others (named entity recognition, various forms of sentiment analysis). We
discuss how these differences may be caused by the different role of word sense
information in each of the tasks. The results highlight the importance of
testing embedding models in real applications
RNNs Implicitly Implement Tensor Product Representations
Recurrent neural networks (RNNs) can learn continuous vector representations
of symbolic structures such as sequences and sentences; these representations
often exhibit linear regularities (analogies). Such regularities motivate our
hypothesis that RNNs that show such regularities implicitly compile symbolic
structures into tensor product representations (TPRs; Smolensky, 1990), which
additively combine tensor products of vectors representing roles (e.g.,
sequence positions) and vectors representing fillers (e.g., particular words).
To test this hypothesis, we introduce Tensor Product Decomposition Networks
(TPDNs), which use TPRs to approximate existing vector representations. We
demonstrate using synthetic data that TPDNs can successfully approximate linear
and tree-based RNN autoencoder representations, suggesting that these
representations exhibit interpretable compositional structure; we explore the
settings that lead RNNs to induce such structure-sensitive representations. By
contrast, further TPDN experiments show that the representations of four models
trained to encode naturally-occurring sentences can be largely approximated
with a bag of words, with only marginal improvements from more sophisticated
structures. We conclude that TPDNs provide a powerful method for interpreting
vector representations, and that standard RNNs can induce compositional
sequence representations that are remarkably well approximated by TPRs; at the
same time, existing training tasks for sentence representation learning may not
be sufficient for inducing robust structural representations.Comment: Accepted to ICLR 201
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