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Terrence Szymanski
Researcher at University College Dublin
Publications - 10
Citations - 411
Terrence Szymanski is an academic researcher from University College Dublin. The author has contributed to research in topics: Newspaper & Word embedding. The author has an hindex of 5, co-authored 10 publications receiving 354 citations. Previous affiliations of Terrence Szymanski include University of Michigan.
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Proceedings Article
Diachronic word embeddings and semantic shifts: a survey
TL;DR: This paper surveys the current state of academic research related to diachronic word embeddings and semantic shifts detection, and proposes several axes along which these methods can be compared, and outlines the main challenges before this emerging subfield of NLP.
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Diachronic word embeddings and semantic shifts: a survey
TL;DR: A survey of the current state of academic research related to diachronic word embeddings and semantic shifts detection can be found in this article, where the authors discuss the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models.
Proceedings ArticleDOI
Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings
TL;DR: It is shown that temporal word analogies can effectively be modeled with diachronic word embeddings, provided that the independent embedding spaces from each time period are appropriately transformed into a common vector space.
Proceedings ArticleDOI
UCD : Diachronic Text Classification with Character, Word, and Syntactic N-grams
Terrence Szymanski,Gerard Lynch +1 more
TL;DR: This work extracts n-gram features from the text at the letter, word, and syntactic level, and uses these to train a classifier on date-labeled training data, and incorporates date probabilities of syntactic features as estimated from a very large external corpus of books.
Posted Content
Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines
TL;DR: In this article, the authors present a software tool that employs state-of-the-art NLP and machine learning techniques to help newspaper editors compose effective headlines for online publication.