Papers by Paul Rayson

15 papers
Towards a Welsh Semantic Annotation System (L18-1)

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Challenge: Automatic semantic annotation of natural language data is an important task in Natural Language Processing.
Approach: They develop a Welsh semantic annotation tool that can be used to analyze Welsh text . it uses Lancaster's USAS semantic classification scheme to tag words with semantic tags .
Outcome: The proposed tool can cover up to 91.78% of words in Welsh text.
MasakhaNER: Named Entity Recognition for African Languages (2021.tacl-1)

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Challenge: (2020) African languages are underrepresented in existing natural language processing datasets, research, and tools due to lack of datasets and reproducible results.
Approach: They propose to create a dataset for named entity recognition (NER) in ten African languages.
Outcome: The results of the first large dataset for named entity recognition (NER) in ten African languages are released to inform future research on African NLP.
The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment (2024.lrec-main)

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Challenge: UNESCO projects that the Igbo language will be endangered by 2025 . primary obstacle in developing dialectal-aware language technologies is lack of comprehensive dialectal datasets.
Approach: They propose to use a multi-dialectal Igbo-English dictionary dataset to enhance the representation of Igbe dialects.
Outcome: The proposed dataset enables machine translation systems to handle dialect variations in sentences.
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms (P19-1)

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Challenge: evaluating models is a non-trivial task and requires extensive data and data splits to produce reliable comparisons.
Approach: They propose a model selection approach that reduces the computational resources required to compare models based on single choices of random seeds.
Outcome: The proposed model selection approach reduces the computational resources required to identify state-of-the-art models from large datasets.
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP Models (C18-1)

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Challenge: a recent study shows that performance estimations are unstable and variable . this makes it difficult to use parameter tuning and model selection .
Approach: They propose to use a less variable CV method to evaluate performance . they propose lower choices of K than are typically seen in the NLP literature .
Outcome: The proposed method can be used for parameter tuning and performance estimation, but it is unstable and unstable.
Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment Analysis (C18-1)

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Challenge: a lack of reproducibility and generalisability is a major threat to scientific development in Natural Language Processing.
Approach: They propose to use a model zoo to document and release language models and published code . they recommend that future replication experiments should consider a variety of datasets .
Outcome: The proposed methods are compared on six English datasets and are based on the results.
Sinhala Encoder-only Language Models and Evaluation (2025.acl-long)

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Challenge: Recent advances in language models (LMs) have produced excellent results in many NLP tasks, but their effectiveness is highly dependent on available pre-training resources.
Approach: They propose to collect the largest monolingual corpus for Sinhala and compile a benchmark and evaluate LMs on it.
Outcome: The proposed language models outperform the popular multilingual LMs in downstream NLP tasks.
Profiling Medical Journal Articles Using a Gene Ontology Semantic Tagger (L18-1)

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Challenge: a growing number of scientific publications are based on sub-divisions and sub-communities of expertise becoming disconnected from each other.
Approach: They propose to examine corpora derived from bodies of genetics literature and use it to make comparisons and improve retrieval methods.
Outcome: The proposed methods will help to make comparisons and improve retrieval methods using domain knowledge via an existing gene ontology.
Infrastructure for Semantic Annotation in the Genomics Domain (2020.lrec-1)

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Challenge: a novel infrastructure for biomedical text mining combines NLP and corpus linguistics methods to provide a comprehensive corpus for literature-based discovery.
Approach: They propose a novel pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature . it uses an updatable Gene Ontology Semantic Tagger and a NLP pipeline scheduler to collect and process the corpus.
Outcome: The proposed infrastructure allows for extreme-scale research on the open access PubMed Central archive.
IgboBERT Models: Building and Training Transformer Models for the Igbo Language (2022.lrec-1)

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Challenge: This paper focuses on building resources for named entity recognition for Igbo, a language mainly spoken in the south eastern part of Nigeria.
Approach: They present a standard Igbo named entity recognition dataset and results from fine-tuning transformer IgbeNER models.
Outcome: The proposed dataset and model improves on the IgboNER task while training and fine-tuning a transformer model with comparatively little Igbe text data.
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology (2020.lrec-1)

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Challenge: Existing ontologies for Arabic are difficult due to the lack of resources and the nature of the Arabic language.
Approach: They propose to build an Arabic ontology that integrates scientific vocabularies with informal equivalents.
Outcome: The proposed ontology integrates scientific vocabularies with informal equivalents in Arabic . it will be automatically generated but the results will be evaluated by a domain expert .
LENS: Learning Entities from Narratives of Skin Cancer (2025.coling-demos)

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Challenge: Learning entities from narratives of skin cancer (LENS) is an automatic entity recognition system built on colloquial writings from skin cancer-related forums.
Approach: They propose to use reddit forums to create an automatic entity recognition system that can be used to predict skin cancer outcomes.
Outcome: LENS achieves an overall entity-level F1 score of 0.561 . other notable results include “CANC_T” (0.747), “STG” (0.888), “POB” (0.914), “GENDER” (0.750), “A/G” (00.646), “EMO” (0.619), and “MHD” (0.503).
LexiDB: Patterns & Methods for Corpus Linguistic Database Management (2020.lrec-1)

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Challenge: LexiDB is a tool for storing, managing and querying corpus data.
Approach: They propose to use LexiDB for storing, managing and querying corpus data.
Outcome: The proposed methods outperform existing tools for corpus queries and storage.
CoFiF Plus: A French Financial Narrative Summarisation Corpus (2022.lrec-1)

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Challenge: Existing corpora for financial narrative summarisation exists in English but there is a significant lack of financial text resources in the French language.
Approach: They propose to use natural language processing to analyse financial documents to find the best summarisation methods.
Outcome: The proposed dataset is the first to provide a comprehensive set of financial text written in French.
Arabic Dialect Identification in the Context of Bivalency and Code-Switching (L18-1)

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Challenge: Existing methods for identifying Arabic dialects require significant amounts of annotated training data which is costly and time consuming to produce.
Approach: They propose a novel approach to Arabic dialect identification using language bivalency and written code-switching to identify Arabic dialects.
Outcome: The proposed method can reach more than 76% and score well (66%) when tested on unseen data.

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