Elaine Zosa


2020

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Discovery Team at SemEval-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection
Matej Martinc | Syrielle Montariol | Elaine Zosa | Lidia Pivovarova
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection. The proposed method is based on clustering of BERT contextual embeddings, followed by a comparison of cluster distributions across time. The best results were obtained by an ensemble of this method and static Word2Vec embeddings. According to the official results, our approach proved the best for Latin in Subtask 2.

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A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval
Elaine Zosa | Mark Granroth-Wilding | Lidia Pivovarova
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

We address the problem of linking related documents across languages in a multilingual collection. We evaluate three diverse unsupervised methods to represent and compare documents: (1) multilingual topic model; (2) cross-lingual document embeddings; and (3) Wasserstein distance.We test the performance of these methods in retrieving news articles in Swedish that are known to be related to a given Finnish article.The results show that ensembles of the methods outperform the stand-alone methods, suggesting that they capture complementary characteristics of the documents

2019

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Word Clustering for Historical Newspapers Analysis
Lidia Pivovarova | Elaine Zosa | Jani Marjanen
Proceedings of the Workshop on Language Technology for Digital Historical Archives

This paper is a part of a collaboration between computer scientists and historians aimed at development of novel tools and methods to improve analysis of historical newspapers. We present a case study of ideological terms ending with -ism suffix in nineteenth century Finnish newspapers. We propose a two-step procedure to trace differences in word usages over time: training of diachronic embeddings on several time slices and when clustering embeddings of selected words together with their neighbours to obtain historical context. The obtained clusters turn out to be useful for historical studies. The paper also discuss specific difficulties related to development historian-oriented tools.

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Multilingual Dynamic Topic Model
Elaine Zosa | Mark Granroth-Wilding
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data.Current DTMs are applicable only to monolingual datasets. In this paper we present the multilingual dynamic topic model (ML-DTM), a novel topic model that combines DTM with an existing multilingual topic modeling method to capture cross-lingual topics that evolve across time. We present results of this model on a parallel German-English corpus of news articles and a comparable corpus of Finnish and Swedish news articles. We demonstrate the capability of ML-DTM to track significant events related to a topic and show that it finds distinct topics and performs as well as existing multilingual topic models in aligning cross-lingual topics.