410 research outputs found
Multi-task Pairwise Neural Ranking for Hashtag Segmentation
Hashtags are often employed on social media and beyond to add metadata to a
textual utterance with the goal of increasing discoverability, aiding search,
or providing additional semantics. However, the semantic content of hashtags is
not straightforward to infer as these represent ad-hoc conventions which
frequently include multiple words joined together and can include abbreviations
and unorthodox spellings. We build a dataset of 12,594 hashtags split into
individual segments and propose a set of approaches for hashtag segmentation by
framing it as a pairwise ranking problem between candidate segmentations. Our
novel neural approaches demonstrate 24.6% error reduction in hashtag
segmentation accuracy compared to the current state-of-the-art method. Finally,
we demonstrate that a deeper understanding of hashtag semantics obtained
through segmentation is useful for downstream applications such as sentiment
analysis, for which we achieved a 2.6% increase in average recall on the
SemEval 2017 sentiment analysis dataset.Comment: 12 pages, ACL 201
#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds
Compounding of natural language units is a very common phenomena. In this
paper, we show, for the first time, that Twitter hashtags which, could be
considered as correlates of such linguistic units, undergo compounding. We
identify reasons for this compounding and propose a prediction model that can
identify with 77.07% accuracy if a pair of hashtags compounding in the near
future (i.e., 2 months after compounding) shall become popular. At longer times
T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This
technique has strong implications to trending hashtag recommendation since
newly formed hashtag compounds can be recommended early, even before the
compounding has taken place. Further, humans can predict compounds with an
overall accuracy of only 48.7% (treated as baseline). Notably, while humans can
discriminate the relatively easier cases, the automatic framework is successful
in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported
Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM
conference on Computer-Supported Cooperative Work and Social Computing (CSCW
2016
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
Breaking domain names such as openresearch into component words open and
research is important for applications like Text-to-Speech synthesis and web
search. We link this problem to the classic problem of Chinese word
segmentation and show the effectiveness of a tagging model based on Recurrent
Neural Networks (RNNs) using characters as input. To compensate for the lack of
training data, we propose a pre-training method on concatenated entity names in
a large knowledge database. Pre-training improves the model by 33% and brings
the sequence accuracy to 85%
Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users
If people with high risk of suicide can be identified through social media
like microblog, it is possible to implement an active intervention system to
save their lives. Based on this motivation, the current study administered the
Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a
leading microblog service provider in China. Two NLP (Natural Language
Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count
(LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract
linguistic features from the Sina Weibo data. We trained predicting models by
machine learning algorithm based on these two types of features, to estimate
suicide probability based on linguistic features. The experiment results
indicate that LDA can find topics that relate to suicide probability, and
improve the performance of prediction. Our study adds value in prediction of
suicidal probability of social network users with their behaviors
Treebanking user-generated content: A proposal for a unified representation in universal dependencies
The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD
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