7,004 research outputs found
Predicting Discourse Structure using Distant Supervision from Sentiment
Discourse parsing could not yet take full advantage of the neural NLP
revolution, mostly due to the lack of annotated datasets. We propose a novel
approach that uses distant supervision on an auxiliary task (sentiment
classification), to generate abundant data for RST-style discourse structure
prediction. Our approach combines a neural variant of multiple-instance
learning, using document-level supervision, with an optimal CKY-style tree
generation algorithm. In a series of experiments, we train a discourse parser
(for only structure prediction) on our automatically generated dataset and
compare it with parsers trained on human-annotated corpora (news domain RST-DT
and Instructional domain). Results indicate that while our parser does not yet
match the performance of a parser trained and tested on the same dataset
(intra-domain), it does perform remarkably well on the much more difficult and
arguably more useful task of inter-domain discourse structure prediction, where
the parser is trained on one domain and tested/applied on another one.Comment: Accepted to EMNLP 2019, 9 page
Improving Distributed Representations of Tweets - Present and Future
Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which poses several challenges such as short length, informal words,
unusual grammar and misspellings. However, there is a lack of prior work which
surveys the representation learning models with a focus on tweets. In this
work, we organize the models based on its objective function which aids the
understanding of the literature. We also provide interesting future directions,
which we believe are fruitful in advancing this field by building high-quality
tweet representation learning models.Comment: To be presented in Student Research Workshop (SRW) at ACL 201
Improving Distributed Representations of Tweets - Present and Future
Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which poses several challenges such as short length, informal words,
unusual grammar and misspellings. However, there is a lack of prior work which
surveys the representation learning models with a focus on tweets. In this
work, we organize the models based on its objective function which aids the
understanding of the literature. We also provide interesting future directions,
which we believe are fruitful in advancing this field by building high-quality
tweet representation learning models.Comment: To be presented in Student Research Workshop (SRW) at ACL 201
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
NLP tasks are often limited by scarcity of manually annotated data. In social
media sentiment analysis and related tasks, researchers have therefore used
binarized emoticons and specific hashtags as forms of distant supervision. Our
paper shows that by extending the distant supervision to a more diverse set of
noisy labels, the models can learn richer representations. Through emoji
prediction on a dataset of 1246 million tweets containing one of 64 common
emojis we obtain state-of-the-art performance on 8 benchmark datasets within
sentiment, emotion and sarcasm detection using a single pretrained model. Our
analyses confirm that the diversity of our emotional labels yield a performance
improvement over previous distant supervision approaches.Comment: Accepted at EMNLP 2017. Please include EMNLP in any citations. Minor
changes from the EMNLP camera-ready version. 9 pages + references and
supplementary materia
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