997 research outputs found
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
Word Embeddings: A Survey
This work lists and describes the main recent strategies for building
fixed-length, dense and distributed representations for words, based on the
distributional hypothesis. These representations are now commonly called word
embeddings and, in addition to encoding surprisingly good syntactic and
semantic information, have been proven useful as extra features in many
downstream NLP tasks.Comment: 10 pages, 2 tables, 1 imag
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
topic-based sentiment analysis.Comment: To appear at NAACL 2018 (long
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