Search Results for author: Lidia Pivovarova

Found 24 papers, 2 papers with code

Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change

no code implementations LChange (ACL) 2022 Mario Giulianelli, Andrey Kutuzov, Lidia Pivovarova

In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such morphosyntactic changes.

Change Detection Language Modelling

Scalable and Interpretable Semantic Change Detection

1 code implementation NAACL 2021 Syrielle Montariol, Matej Martinc, Lidia Pivovarova

We propose a novel scalable method for word usage-change detection that offers large gains in processing time and significant memory savings while offering the same interpretability and better performance than unscalable methods.

Change Detection

Topic modelling discourse dynamics in historical newspapers

no code implementations20 Nov 2020 Jani Marjanen, Elaine Zosa, Simon Hengchen, Lidia Pivovarova, Mikko Tolonen

This paper addresses methodological issues in diachronic data analysis for historical research.

Topic Models

Capturing Evolution in Word Usage: Just Add More Clusters?

no code implementations18 Jan 2020 Matej Martinc, Syrielle Montariol, Elaine Zosa, Lidia Pivovarova

The way the words are used evolves through time, mirroring cultural or technological evolution of society.

Change Detection

Word Clustering for Historical Newspapers Analysis

no code implementations RANLP 2019 Lidia Pivovarova, Elaine Zosa, Jani Marjanen

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.

Comparison of Representations of Named Entities for Document Classification

no code implementations WS 2018 Lidia Pivovarova, Roman Yangarber

We explore representations for multi-word names in text classification tasks, on Reuters (RCV1) topic and sector classification.

Classification Document Classification +3

DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings

no code implementations SEMEVAL 2018 Dmitry Kravchenko, Lidia Pivovarova

We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence.

Emotion Classification Word Embeddings

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