Lexical Semantic Change: Models, Data and Evaluation
Lexical Semantic Change (LSC) is a phenomenon associated with the diachronic evolution of the meanings of a word. In historical linguistics, it is a well-known problem, which is recently gaining momentum also in the Natural Language Processing (NLP) and Computational Lingustics (CL) communities, thanks to the availability of both large diachronic corpora and evaluation benchmarks.
This tutorial will present an overview of the current approaches, problems and challenges in this research field. The tutorial will consist of two parts. First, we will provide an introduction to Lexical Semantic Change and to the available resources (corpora and data sets) for the study of meanings in diachrony. We will highlight issues in the creation and use of diachronic corpora, as well as different procedure for the annotation of data. In the second part, we will introduce the current state-of-the-art approaches for the automatic detection of LSC and we will provide an hands-on section on available systems and tools.
Registration is open https://lrec2022.lrec-conf.org/en/registration/
The schedule is available here https://lrec2022.lrec-conf.org/en/workshops-and-tutorials/ws-tut-schedule/
Pierpaolo Basile, University of Bari, Italy
Annalina Caputo, Dublin City University, Ireland
Pierluigi Cassotti, University of Bari, Italy
Rossella Varvara, Université de Fribourg, Switzerland
Diachronic Corpora (e.g. The New York Times Annotated Corpus , Corpus of Contemporary American English , L’Unità corpus )
Co-occurrence matrices (e.g. Dukweb )
Ngrams (e.g. Google Ngrams)
Annotation of Lexical Semantic Changes
[10:00-10:30] Models Part I
- PPMI matrix factorization
- Word2Vec Skip-gram with Negative Sampling (SGNS)
- BERT-based models
Post-alignment models (e.g. Orthogonal Procrustes )
Jointly alignment models (e.g. Temporal Random Indexing (TRI) , Temporal Word Embedding with a Compass (TWEC) , Temporal Referencing (TR) , Dynamic Word Embedding (DWE) , Dynamic Bernoulli Embedding (DBE) )
[11:00-11:45] Models Part II
- Temporal Attention
- Deep Mistake
- Gloss Reader
- Local Neighborhood measure
- Word Sense Induction
- Grammatical Features
Binary task (e.g. Semeval 2020 Task 1 Subtask 1 , DIACR-Ita )
Graded task (e.g. Semeval 2020 Task 1 Subtask 2 , RushiftEval )
Temporal Analogies 
Lexical Semantic Change Discovery 
Word2Vec Skip-grams with Negative Sampling (SGNS) and Orthogonal Procrustes (OP) (Colab Notebook)
BERT for Lexical Semantic Change (Colab Notebook)
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