Challenge: a new version of a dictionary for the humanities provides time-variant information on word meanings and lexical emotions.
Approach: They propose an extended version of JeSemE for visualizing computationally derived time-variant information on word meanings and lexical emotions.
Outcome: The proposed tool combines state-of-the-art distributional semantics with a nuanced model of human emotions.

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Lexi: A tool for adaptive, personalized text simplification (C18-1)

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Challenge: Existing research on text simplification has aimed to develop generic solutions . instead, we need to develop customized simplification systems for individual users .
Approach: They propose a framework for adaptive lexical simplification and introduce Lexi, a free open-source tool for personalized text simplification.
Outcome: The proposed framework is based on a free open-source tool for adaptive, personalized text simplification.
Measuring and Modeling Language Change (N19-5)

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Challenge: This tutorial will help researchers answer questions fundamental to the social sciences and humanities .
Approach: This tutorial is designed to help researchers answer questions in the social sciences and humanities . it synthesizes recent computational techniques for handling and modeling temporal data .
Outcome: The tutorial will synthesize recent techniques for handling and modeling temporal data, such as dynamic word embeddings, and identify useful tools for social scientists and digital humanities scholars.
TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora (2021.naacl-demos)

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Challenge: Existing studies using distributional embeddings to study language use have focused on quantitative measurement of change, rather than inter-corpus analysis.
Approach: They propose a system that allows comparative analysis of corpora using embeddings.
Outcome: The proposed system can be used for categorical and comparative analysis of text corpora.
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)

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Challenge: Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison.
Approach: They propose to extend benchmark models on a common state-of-the-art evaluation task . they also demonstrate that the same evaluation task and modelling approaches can be utilised for synchronic detection of domain-specific sense divergences in the field of term extraction.
Outcome: The proposed model can be utilised for the detection of domain-specific sense divergences in the field of term extraction.
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin. (2020.lrec-1)

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Challenge: a new paper aims to provide sentiment analysis tools for ancient languages . the current sentiment analysis resources only cover modern languages based on textual typologies .
Approach: They propose to use manually-curated Latin lexicons to evaluate sentiment analysis tools . they propose a gold standard and a silver standard for evaluating lexical items .
Outcome: The proposed lexicons are evaluated using a gold standard and a silver standard for sentiment analysis.
Diachronic word embeddings and semantic shifts: a survey (C18-1)

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Challenge: Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing.
Approach: They propose several axes along which these methods can be compared and propose a framework for comparison.
Outcome: The proposed methods are compared with existing methods and outline their main challenges and potential applications.
A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse Corpus (2022.lrec-1)

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Challenge: Using a linguistic perspective, emotion annotation is considered a difficult task because of the lack of consensus on emotional categories, the fuzziness of boundaries between them or the great variability of emotion expressions types.
Approach: They propose a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in french.
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Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (2020.coling-main)

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Challenge: Emotion lexicons provide information about associations between words and emotions.
Approach: They use crowdsourcing to annotate words with Plutchik's 8 basic emotions, providing binary labels.
Outcome: The proposed lexicons provide information about associations between words and emotions . the lexiconics are useful in emotional analyses of reviews, literary texts, and posts on social media .
Toward Sentiment Aware Semantic Change Analysis (2024.eacl-srw)

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Challenge: Current approaches to analyze semantic change are lagging behind . current methods only detect semantic change as a binary classification or graded change scores .
Approach: They propose to augment models of semantic change with sentiment information . they demonstrate that existing models extract reliable sentiment information from historical corpora .
Outcome: The proposed approach shows mixed results on the English SemEval of Lexical Semantic Change and its associated historical corpora.
Computational modeling of semantic change (2024.eacl-tutorials)

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Challenge: Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves.
Approach: They propose to categorise the types of change, the causes and the mechanisms underlying the different types of changes using large diachronic corpora and evaluation benchmarks.
Outcome: In historical linguistics, tools and methods have been developed to analyse the process . they include categorisations of types of change, causes and mechanisms . but traditional methods, while informative, are often based on small, carefully curated samples.

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