Enriching Word Usage Graphs with Cluster Definitions: A Detailed Analysis

Core Concepts

Generating human-readable sense definitions for word usage graphs enhances semantic analysis and aids in explainable semantic change modeling.

Abstract

The content discusses the generation of human-readable sense definitions for word usage graphs (WUGs) to improve semantic analysis. It covers the importance of sense definitions in NLP tasks, the existing resources for semantic change modeling, the methodology of generating definitions, data description, evaluation results, error analysis, and future work.
Introduction and Related Work

Most words are polysemous in natural languages.
NLP tasks like word sense induction and disambiguation rely on valuable resources related to word senses.
Semantic change modeling heavily uses Word Usage Graphs (WUGs) annotated with semantic proximity judgments.
Existing WUGs lack human-readable sense labels for clusters.
Data Description

Word usage graphs for English, German, Norwegian, and Russian are enriched with human-readable sense definitions.
Definition datasets are fine-tuned on various resources for different languages.
Definition Generators

Three methods are presented: Lesk, GlossReader, and DefGen.
DefGen outperforms baselines in accuracy for generating human-readable definitions.
Evaluation and Results

Human evaluation shows DefGen's superiority in generating accurate cluster labels.
Error analysis reveals common mistakes in generated definitions.
Conclusion

Generating human-readable sense definitions enhances semantic analysis and semantic change modeling.
DefGen method shows promise for generating definitions across languages.
Future work includes multilingual fine-tuning and expanding to more languages.

Stats

The conducted human evaluation has shown that these definitions match the existing clusters in WUGs better than the definitions chosen from WordNet by two baseline systems.
Table 1 provides the main statistics of the word usage graphs we employ.
Table 2 shows the statistics of the definition datasets for fine-tuning.
Table 3 shows the performance of mT0-based definition generators on the validation sets.
Table 4 displays the results of human evaluation in the 'guess the cluster by definition' task.
Tables 5, 6, and 7 categorize erroneous definitions by error type.
Table 8 provides examples of good definition instances.
Table 9 showcases borderline definition examples.
Table 10 presents examples of bad definition instances.

Quotes

"The resulting enriched datasets can be extremely helpful for moving on to explainable semantic change modeling."
"Our contribution is twofold: making existing word usage graphs more useful and evaluating definition generation methods on multiple languages."
"Generated definitions can be used as convenient and interpretable contextualized representations for various NLP tasks."

Key Insights Distilled From

Enriching Word Usage Graphs with Cluster Definitions

by Mariia Fedor... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18024.pdf

Deeper Inquiries

How can the generated human-readable sense definitions impact other NLP tasks beyond semantic change modeling?

The generated human-readable sense definitions can have a significant impact on various NLP tasks beyond semantic change modeling. One key application is in Word Sense Disambiguation (WSD), where the definitions can provide valuable context for disambiguating the meaning of words in different contexts. By having clear and interpretable definitions for word senses, NLP models can better understand and differentiate between the various meanings of a word, leading to improved accuracy in tasks like machine translation, information retrieval, and sentiment analysis. Additionally, these definitions can enhance the performance of tasks like Named Entity Recognition (NER) by providing more nuanced information about the entities being identified. Overall, the human-readable sense definitions serve as rich contextual representations that can enhance the performance of a wide range of NLP applications.

How might the concept of explainable semantic change modeling influence broader discussions on language evolution and understanding?

The concept of explainable semantic change modeling can have profound implications for broader discussions on language evolution and understanding. By providing clear and interpretable definitions for word senses over time, researchers and linguists can gain deeper insights into how language evolves and adapts. These human-readable sense definitions can shed light on the subtle shifts in meaning that occur within languages, offering a more transparent view of semantic changes over different time periods. This transparency can lead to a better understanding of cultural shifts, historical events, and societal changes that influence language usage. Furthermore, by making semantic changes more explainable and accessible, the concept of explainable semantic change modeling can foster interdisciplinary collaborations between linguists, historians, and computational researchers, enriching our understanding of language evolution and its impact on society.