Detection of Non-recorded Word Senses in English and Swedish: A Comprehensive Study

Core Concepts

The study focuses on detecting non-recorded word senses using a pre-trained Word-in-Context embedder, significantly increasing the detection of non-recorded senses compared to random sampling.

Abstract

The study addresses the task of Unknown Sense Detection in English and Swedish by comparing sense entries with word usages from corpora. It uses a pre-trained Word-in-Context embedder to model the task in a few-shot scenario, showing promising results in detecting non-recorded senses. The research highlights the importance of dictionary maintenance and the challenges posed by evolving language use over time. By utilizing human annotations for evaluation, the study provides insights into improving models for detecting unknown word senses.

Stats

Compared to a random sample from a corpus, our model considerably increases the detected number of word usages with non-recorded senses.
For English, out of 473 usages, 45 are labeled unassigned (9.5%).
For Swedish, out of 674 usages, 95 are labeled unassigned (16.6%).
In Phase II predictions for Swedish, out of 1000 predictions, almost two-thirds were correct according to human judgment.

Quotes

"Automatic methods help update dictionaries convincingly."
"Our method considerably increases the chance to find non-recorded word senses in corpus usages."
"Our goal was to automatically detect non-recorded word senses based on realistic sense inventory."
"The study provides insights into improving models for detecting unknown word senses."

Key Insights Distilled From

Detection of Non-recorded Word Senses in English and Swedish

by Jona... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.02285.pdf

Deeper Inquiries

How can models be improved to better detect multi-word expressions as non-recorded senses?

To improve the detection of multi-word expressions as non-recorded senses, models can incorporate more sophisticated techniques for identifying and processing these complex linguistic units. One approach could involve enhancing the word usage sampling algorithm to better capture multi-word expressions by considering collocations or phrases rather than just individual words. Additionally, models could benefit from incorporating syntactic and semantic analysis tools that are specifically designed to handle multi-word expressions effectively. By leveraging advanced natural language processing methods that focus on capturing the nuances of phrase-level semantics, models can enhance their ability to detect non-recorded senses embedded within multi-word expressions.

What implications does this research have for future developments in natural language processing tasks?

This research has significant implications for advancing natural language processing tasks, particularly in the realm of lexical semantics and dictionary maintenance. By demonstrating the effectiveness of using pre-trained contextualized embeddings and human annotations to detect non-recorded word senses, this study paves the way for developing more accurate and efficient algorithms for sense disambiguation and unknown sense detection. The findings underscore the importance of leveraging both automated computational approaches and human expertise in refining lexical resources across different languages. Future developments may focus on optimizing model architectures, improving data preprocessing techniques, and exploring innovative strategies for integrating machine learning with human annotation processes.

How can these findings be applied to enhance dictionary maintenance practices beyond English and Swedish languages?

The findings from this research offer valuable insights that can be applied to enhance dictionary maintenance practices across various languages beyond English and Swedish. The methodology developed in this study, which combines automated modeling with human annotation for detecting non-recorded word senses, serves as a robust framework that can be adapted to other linguistic contexts. By utilizing similar approaches tailored to specific languages' dictionaries and corpora, lexicographers can identify missing or outdated sense entries more effectively. This approach facilitates continuous updates and improvements in multilingual dictionaries by leveraging computational tools alongside expert knowledge. Implementing similar methodologies in diverse linguistic settings enables comprehensive dictionary maintenance practices that cater to a wide range of language users worldwide.