mlsanityData to 14 Oct 2026

TAPHSIR: Towards AnaPHoric Ambiguity Detection and ReSolution In Requirements

arXiv 2206.10227 ↗First posted 21 Jun 2022cs.SE cs.CLcited by 3

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Abstract (arXiv)

We introduce TAPHSIR, a tool for anaphoric ambiguity detection and anaphora resolution in requirements. TAPHSIR facilities reviewing the use of pronouns in a requirements specification and revising those pronouns that can lead to misunderstandings during the development process. To this end, TAPHSIR detects the requirements which have potential anaphoric ambiguity and further attempts interpreting anaphora occurrences automatically. TAPHSIR employs a hybrid solution composed of an ambiguity detection solution based on machine learning and an anaphora resolution solution based on a variant of the BERT language model. Given a requirements specification, TAPHSIR decides for each pronoun occurrence in the specification whether the pronoun is ambiguous or unambiguous, and further provides an automatic interpretation for the pronoun. The output generated by TAPHSIR can be easily reviewed and validated by requirements engineers. TAPHSIR is publicly available on Zenodo (DOI: 10.5281/zenodo.5902117).

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 21 Jun 2022.

Builds on

5 of 19 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  4. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 834
  5. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.

3 papers.