61 research outputs found
GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering
This paper describes the system proposed for the SemEval-2020 Task 1:
Unsupervised Lexical Semantic Change Detection. We focused our approach on the
detection problem. Given the semantics of words captured by temporal word
embeddings in different time periods, we investigate the use of unsupervised
methods to detect when the target word has gained or loosed senses. To this
end, we defined a new algorithm based on Gaussian Mixture Models to cluster the
target similarities computed over the two periods. We compared the proposed
approach with a number of similarity-based thresholds. We found that, although
the performance of the detection methods varies across the word embedding
algorithms, the combination of Gaussian Mixture with Temporal Referencing
resulted in our best system
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
Dynamic topic modeling facilitates the identification of topical trends over
time in temporal collections of unstructured documents. We introduce a novel
unsupervised neural dynamic topic model named as Recurrent Neural
Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each
time influence the topic discovery in the subsequent time steps. We account for
the temporal ordering of documents by explicitly modeling a joint distribution
of latent topical dependencies over time, using distributional estimators with
temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP
research, we demonstrate that compared to state-of-the art topic models, RNNRSM
shows better generalization, topic interpretation, evolution and trends. We
also introduce a metric (named as SPAN) to quantify the capability of dynamic
topic model to capture word evolution in topics over time.Comment: In Proceedings of the 16th Annual Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies (NAACL-HLT 2018
Discovering Barriers to Opioid Addiction Treatment from Social Media: A Similarity Network-Based Deep Learning Approach
Opioid use disorder (OUD) refers to the physical and psychological reliance on opioids. OUD costs the US healthcare systems $504 billion annually and poses significant mortality risk for patients. Understanding and mitigating the barriers to OUD treatment is a high-priority area. Current OUD treatment studies rely on surveys with low response rate because of social stigma. In this paper, we explore social media as a new data source to study OUD treatments. We develop the SImilarity Network-based DEep Learning (SINDEL) to discover barriers to OUD treatment from the patient narratives and address the challenge of morphs. SINDEL reaches an F1 score of 76.79%. Thirteen types of OUD treatment barriers were identified and verified by domain experts. This study contributes to IS literature by proposing a novel deep-learning-based analytical approach with impactful implications for health practitioners
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