786 research outputs found
SMDDH: Singleton Mention detection using Deep Learning in Hindi Text
Mention detection is an important component of coreference resolution system,
where mentions such as name, nominal, and pronominals are identified. These
mentions can be purely coreferential mentions or singleton mentions
(non-coreferential mentions). Coreferential mentions are those mentions in a
text that refer to the same entities in a real world. Whereas, singleton
mentions are mentioned only once in the text and do not participate in the
coreference as they are not mentioned again in the following text. Filtering of
these singleton mentions can substantially improve the performance of a
coreference resolution process. This paper proposes a singleton mention
detection module based on a fully connected network and a Convolutional neural
network for Hindi text. This model utilizes a few hand-crafted features and
context information, and word embedding for words. The coreference annotated
Hindi dataset comprising of 3.6K sentences, and 78K tokens are used for the
task. In terms of Precision, Recall, and F-measure, the experimental findings
obtained are excellent
Coreference Resolution System for Indonesian Text with Mention Pair Method and Singleton Exclusion using Convolutional Neural Network
Neural network has shown promising performance on coreference resolution
systems that uses mention pair method. With deep neural network, it can learn
hidden and deep relations between two mentions. However, there is no work on
coreference resolution for Indonesian text that uses this learning technique.
The state-of-the-art system for Indonesian text only states the use of lexical
and syntactic features can improve the existing coreference resolution system.
In this paper, we propose a new coreference resolution system for Indonesian
text with mention pair method that uses deep neural network to learn the
relations of the two mentions. In addition to lexical and syntactic features,
in order to learn the representation of the mentions words and context, we use
word embeddings and feed them to Convolutional Neural Network (CNN).
Furthermore, we do singleton exclusion using singleton classifier component to
prevent singleton mentions entering any entity clusters at the end. Achieving
67.37% without singleton exclusion, 63.27% with trained singleton classifier,
and 75.95% with gold singleton classifier on CoNLL average F1 score, our
proposed system outperforms the state-of-the-art system
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