Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Representation Learning for Weakly Supervised Relation Extraction   Recent years have seen rapid development in Information Extraction, as well
as its subtask, Relation Extraction. Relation Extraction is able to detect
semantic relations between entities in sentences. Currently, many efficient
approaches have been applied to relation extraction tasks. Supervised learning
approaches especially have good performance. However, there are still many
difficult challenges. One of the most serious problems is that manually labeled
data is difficult to acquire. In most cases, limited data for supervised
approaches equals lousy performance. Thus here, under the situation with only
limited training data, we focus on how to improve the performance of our
supervised baseline system with unsupervised pre-training. Feature is one of
the key components in improving the supervised approaches. Traditional
approaches usually apply hand-crafted features, which require expert knowledge
and expensive human labor. However, this type of feature might suffer from data
sparsity: when the training set size is small, the model parameters might be
poorly estimated. In this thesis, we present several novel unsupervised
pre-training models to learn the distributed text representation features,
which are encoded with rich syntactic-semantic patterns of relation
expressions. The experiments have demonstrated that this type of feature,
combine with the traditional hand-crafted features, could improve the
performance of the logistic classification model for relation extraction,
especially on the classification of relations with only minor training
instances.

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The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
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The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th