18,117 research outputs found
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
Interactive Attention Networks for Aspect-Level Sentiment Classification
Aspect-level sentiment classification aims at identifying the sentiment
polarity of specific target in its context. Previous approaches have realized
the importance of targets in sentiment classification and developed various
methods with the goal of precisely modeling their contexts via generating
target-specific representations. However, these studies always ignore the
separate modeling of targets. In this paper, we argue that both targets and
contexts deserve special treatment and need to be learned their own
representations via interactive learning. Then, we propose the interactive
attention networks (IAN) to interactively learn attentions in the contexts and
targets, and generate the representations for targets and contexts separately.
With this design, the IAN model can well represent a target and its collocative
context, which is helpful to sentiment classification. Experimental results on
SemEval 2014 Datasets demonstrate the effectiveness of our model.Comment: Accepted by IJCAI 201
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