Thailand

This paper explores the evolution of word meanings in 19th-century Spanish texts, with an emphasis on Latin American Spanish, using computational linguistics techniques. It addresses the Semantic Shift Detection (SSD) task, which is crucial for understanding linguistic evolution, particularly in historical contexts. The study focuses on analyzing a set of Spanish target words. To achieve this, a 19th-century Spanish corpus is constructed, and a customizable pipeline for SSD tasks is developed. This pipeline helps find the senses of a word and measure their semantic change between two corpora using fine-tuned BERT-like models with old Spanish texts for both Latin American and general Spanish cases. The results provide valuable insights into the cultural and societal shifts reflected in language changes over time.

ACL 2024

Historical Ink: Semantic Shift Detection for 19th Century Spanish

dwug

latin-america

old spanish

context embedding

sense

semantic shift detection

corpus

bert

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

There has been a surge of interest in computational modeling of semantic change. The foci of previous works are on detecting and interpreting word senses gained over time; however, it remains unclear whether the gained senses are covered by dictionaries. In this work, we aim to fill this research gap by comparing detected word senses with dictionary sense inventories in order to bridge between the communities of lexical semantic change detection and lexicography. We evaluate our system in the AXOLOTL-24 shared task for Finnish, Russian and German languages \cite{fedorova-etal-2024-axolotl}. Our system is fully unsupervised. It leverages a graph-based clustering approach to predict mappings between unknown word usages and dictionary entries for Subtask 1, and generates dictionary-like definitions for those novel word usages through the state-of-the-art Large Language Models such as GPT-4 and LLaMA-3 for Subtask 2. In Subtask 1, our system outperforms the baseline system by a large margin, and it offers interpretability for the mapping results by distinguishing between matched and unmatched (novel) word usages through our graph-based clustering approach. Our system ranks first in Finnish and German, and ranks second in Russian on the Subtask 2 test-phase leaderboard. These results show the potential of our system in managing dictionary entries, particularly for updating dictionaries to include novel sense entries. Our code and data are made publicly available at https://github.com/xiaohemaikoo/axolotl24-ABDN-NLP.

Presence or Absence: Are Unknown Word Usages in Dictionaries?

This paper is concerned with annotating the syntax of ancient Chinese, which is a series of languages in the same development process. The major challenge is to ensure the annotations of languages at different stages are comparable. To this end, we propose a feature-based approach that integrates the deductive feature design from the Chomskyan school and the inductive feature design from traditional philological studies. We demonstrate the effectiveness of our approach by annotating a collection of representative sentences that cover various linguistic phenomena that are extensively discussed in the literature. As a result, we establish a corpus of 673 (for now) ancient Chinese sentences paired with syntactic analyses, covering from 700s B.C.E. to 1900s C.E. The corpus can be utilised as a guideline for future large-scale TreeBanking.

A Feature-Based Approach to Annotate the Syntax of Ancient Chinese

This paper describes the organization and findings of AXOLOTL'24, the first multilingual explainable semantic change modeling shared task. We present new sense-annotated diachronic semantic change datasets for Finnish and Russian which were employed in the shared task, along with a surprise test-only German dataset borrowed from an existing source. The setup of AXOLOTL'24 is new to the semantic change modeling field, and involves subtasks of identifying unknown (novel) senses and providing dictionary-like definitions to these senses. The methods of the winning teams are described and compared, thus paving a path towards explainability in computational approaches to historical change of meaning.

AXOLOTL’24 Shared Task on Multilingual Explainable Semantic Change Modeling

This paper investigates edge induction as a method for augmenting Word Usage Graphs, in which word usages (nodes) are connected through scores (edges) representing semantic relatedness. Clustering (densely) annotated WUGs can be used as a way to find senses of a word without relying on traditional word sense annotation. However, annotating all or a majority of pairs of usages is typically infeasible, resulting in sparse graphs and, likely, lower quality senses. In this paper, we ask if filling out WUGs with edges predicted from the human annotated edges improves the eventual clusters. We experiment with edge induction models that use structural features of the existing sparse graph, as well as those that exploit textual (distributional) features of the usages. We find that in both cases, inducing edges prior to clustering improves correlation with human sense-usage annotation across three different clustering algorithms and languages.

Improving Word Usage Graphs with Edge Induction

We present our submission to the AXOLOTL-24 shared task. The shared task comprises two subtasks: identifying new senses that words gain with time (when comparing newer and older time periods) and producing the definitions for the identified new senses. We implemented a conceptually simple and computationally inexpensive solution to both subtasks. We trained adapter-based binary classification models to match glosses with usage examples and leveraged the probability output of the models to identify novel senses. The same models were used to match examples of novel sense usages with Wiktionary definitions. Our submission attained third place on the first subtask and the first place on the second subtask.

TartuNLP @ AXOLOTL-24: Leveraging Classifier Output for New Sense Detection in Lexical Semantics

Etymology, and the field of lexicography, is often constrained by unstructured data formats buried in scholarly articles and dictionaries. This paper presents a methodology and an empirical study for creating a structured etymological dataset suitable for computational analysis. Using data from the Online Etymology Dictionary (Etymonline), we manually annotated a subset of entries to establish a high-quality ground-truth dataset and fine-tuned the FLAN-T5-base model to extract structured etymological relationships automatically. The resulting dataset contains over 103,000 relationships covering 63,603 English lexical terms. Our findings emphasise feasibility of using large language models for structuring lexicographical data, exploring the transferability of the model to other dictionary datasets with no additional manual annotation.

EtymoLink: A Structured English Etymology Dataset

This paper explores the intersection of lexical complexity prediction and lexical semantic change detection. We investigate the potential connection between changes in lexical complexity and lexical semantics, aiming to uncover how these two aspects of language evolution are intertwined. Our findings indicate that lexical complexity models human annotator uncertainty surprisingly well. Further, we find a moderate correlation between changes in lexical complexity and graded lexical semantic change. This highlights the potential for leveraging lexical complexity for lexical semantic change detection.

Complexity and Indecision: A Proof-of-Concept Exploration of Lexical Complexity and Lexical Semantic Change

This paper describes our solution of the first subtask from the AXOLOTL-24 shared task on Semantic Change Modeling. The goal of this subtask is to distribute a given set of usages of an ambiguous word from a newer time period between senses of this word from an older time period given as a set of sense definitions and some undefined number of clusters that should represent gained senses of this word. We propose and experiment with three new methods solving this task. Our methods achieve SOTA results according to both official metrics of the shared task. Additionally, we develop a model that can tell if a given word usage is not described by any of the provided sense definitions. This model serves as a component in one of our methods, but can potentially be useful on its own.

Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling

Computational and human perception are often considered separate approaches for studying sound changes over time; few works have touched on the intersection of both. To fill this research gap, we provide a pioneering review contrasting computational with human perception from the perspectives of methods and tasks. Overall, computational approaches rely on computer-driven models to perceive historical sound changes on etymological datasets, while human approaches use listener-driven models to perceive ongoing sound changes on recording corpora. Despite their differences, both approaches complement each other on phonetic and acoustic levels, showing the potential to achieve a more comprehensive perception of sound change. Moreover, we call for a comparative study on the datasets used by both approaches to investigate the influence of historical sound changes on ongoing changes. Lastly, we discuss the applications of sound change in computational linguistics, and point out that perceiving sound change alone is insufficient, as many processes of language change are complex, with entangled changes at syntactic, semantic, and phonetic levels.

Exploring Sound Change Over Time: A Review of Computational and Human Perception

Lexical Semantic Change Detection (LSCD) aims to detect language change from a diachronic corpus over time. We can see that over the last two decades there has been a surge in research dealing with the LSC Detection. Recently, a series of methods especially contextualized word embeddings have been widely established to address this task. While several studies have investigated LSCD using large language models (LLMs), an evaluation of prompt engineering techniques, such as few-shot learning with different in-context examples for improving the LSCD performance is required. In this study, we examine the few-shot learning ability of GPT-4 to detect semantic changes in the Chinese language change evaluation dataset ChiWUG. We show that our LLM-based solution improves the GCD evaluation metric on the ChiWUG benchmark compared to the previously top-performing pre-trained system. The result suggests that using GPT-4 with three-shot learning with hand-picked demonstrations achieves the best performance among our different prompts.

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Presence or Absence: Are Unknown Word Usages in Dictionaries?

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