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Encoder-Attention-Based Automatic Term Recognition (EA-ATR)

Authors Sampritha H. Manjunath, John P. McCrae

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Author Details

Sampritha H. Manjunath
  • Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
John P. McCrae
  • Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

Funding

This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2 (Insight_2).

Acknowledgements

We would like to thank the reviewers for helpful comments and insightful feedback.

Cite As Get BibTex

Sampritha H. Manjunath and John P. McCrae. Encoder-Attention-Based Automatic Term Recognition (EA-ATR). In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 23:1-23:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/OASIcs.LDK.2021.23

Abstract

Automated Term Recognition (ATR) is the task of finding terminology from raw text. It involves designing and developing techniques for the mining of possible terms from the text and filtering these identified terms based on their scores calculated using scoring methodologies like frequency of occurrence and then ranking the terms. Current approaches often rely on statistics and regular expressions over part-of-speech tags to identify terms, but this is error-prone. We propose a deep learning technique to improve the process of identifying a possible sequence of terms. We improve the term recognition by using Bidirectional Encoder Representations from Transformers (BERT) based embeddings to identify which sequence of words is a term. This model is trained on Wikipedia titles. We assume all Wikipedia titles to be the positive set, and random n-grams generated from the raw text as a weak negative set. The positive and negative set will be trained using the Embed, Encode, Attend and Predict (EEAP) formulation using BERT as embeddings. The model will then be evaluated against different domain-specific corpora like GENIA - annotated biological terms and Krapivin - scientific papers from the computer science domain.

Subject Classification

ACM Subject Classification
  • Information systems → Top-k retrieval in databases
  • Computing methodologies → Information extraction
  • Computing methodologies → Neural networks
Keywords
  • Automatic Term Recognition
  • Term Extraction
  • BERT
  • EEAP
  • Deep Learning for ATR

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