Search Results for author:
Found 49 papers, 9 papers with code
A Comparison between Named Entity Recognition Models in the Biomedical Domain
The domain-specialised application of Named Entity Recognition (NER) is known as Biomedical NER (BioNER), which aims to identify and classify biomedical concepts that are of interest to researchers, such as genes, proteins, chemical compounds, drugs, mutations, diseases, and so on.
Benchmarking ASR Systems Based on Post-Editing Effort and Error Analysis
This paper offers a comparative evaluation of four commercial ASR systems which are evaluated according to the post-editing effort required to reach “publishable” quality and according to the number of errors they produce.
Interactive Models for Post-Editing
Despite the increasingly good quality of Machine Translation (MT) systems, MT outputs require corrections.
Fake News Detection for Portuguese with Deep Learning
The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading information.
Cross-Lingual Named Entity Recognition via FastAlign: a Case Study
In this paper, we compare the performance of two alignment methods, Grow-diag-final-and and Intersect Symmetrisation heuristics, to exploit the annotation projection of English-Brazilian Portuguese bilingual corpus to detect named entities in Brazilian Portuguese.
Translationese in Russian Literary Texts
We expect that literary translations from typologically distant languages should exhibit more translationese, and the fingerprints of individual source languages (and their families) are traceable in translations.
Paragraph Similarity Matches for Generating Multiple-choice Test Items
Although the research on the automatic or semi-automatic generation of multiple-choice test items has been conducted since the beginning of this millennium, most approaches focus on generating questions from a single sentence.
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain
The goal of the Qur’an QA 2022 shared task is to fill this gap by producing state-of-the-art question answering and reading comprehension research on Qur’an.
Fiction in Russian Translation: A Translationese Study
The texts are represented with frequency-based features that capture structural and lexical properties of language.
Towards New Generation Translation Memory Systems
Despite the enormous popularity of Translation Memory systems and the active research in the field, their language processing features still suffer from certain limitations.
From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP
Researchers have relegated natural language processing tasks to Transformer-type models, particularly generative models, because these models exhibit high versatility when performing generation and classification tasks.
Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)
The first Workshop on Language Models for Low-Resource Languages (LoResLM 2025) was held in conjunction with the 31st International Conference on Computational Linguistics (COLING 2025) in Abu Dhabi, United Arab Emirates.
DORE: A Dataset For Portuguese Definition Generation
In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100, 000 definitions.
Can Model Fusing Help Transformers in Long Document Classification? An Empirical Study
Text classification is an area of research which has been studied over the years in Natural Language Processing (NLP).
Deep Learning Methods for Extracting Metaphorical Names of Flowers and Plants
The domain of Botany is rich with metaphorical terms.
Transformer-based Detection of Multiword Expressions in Flower and Plant Names
In this paper, we explore state-of-the-art neural transformers in the task of detecting MWEs in flower and plant names.
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain
The goal of the Qur'an QA 2022 shared task is to fill this gap by producing state-of-the-art question answering and reading comprehension research on Qur'an.
Biographical: A Semi-Supervised Relation Extraction Dataset
The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata.
An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers
Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models.
RGCL at SemEval-2020 Task 6: Neural Approaches to DefinitionExtraction
This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2.
TransQuest: Translation Quality Estimation with Cross-lingual Transformers
Recent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures.
RGCL at SemEval-2020 Task 6: Neural Approaches to Definition Extraction
This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2.
TransQuest at WMT2020: Sentence-Level Direct Assessment
This paper presents the team TransQuest's participation in Sentence-Level Direct Assessment shared task in WMT 2020.
Classifying Referential and Non-referential It Using Gaze
When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones.
What Influences the Features of Post-editese? A Preliminary Study
While a number of studies have shown evidence of translationese phenomena, that is, statistical differences between original texts and translated texts (Gellerstam, 1986), results of studies searching for translationese features in postedited texts (what has been called {''}posteditese{''} (Daems et al., 2017)) have presented mixed results.
Semantic Textual Similarity with Siamese Neural Networks
Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction.
Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations
Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction.
Contextualised Word Representations
Information Retrieval
+6
RGCL-WLV at SemEval-2019 Task 12: Toponym Detection
This article describes the system submitted by the RGCL-WLV team to the SemEval 2019 Task 12: Toponym resolution in scientific papers.
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture.
WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B.
Translation Memory Systems Have a Long Way to Go
These tools operate on fuzzy (surface) matching mostly and cannot benefit from already translated texts which are synonymous to (or paraphrased versions of) the text to be translated.
Effects of Lexical Properties on Viewing Time per Word in Autistic and Neurotypical Readers
Eye tracking studies from the past few decades have shaped the way we think of word complexity and cognitive load: words that are long, rare and ambiguous are more difficult to read.
Investigating the Opacity of Verb-Noun Multiword Expression Usages in Context
This study investigates the supervised token-based identification of Multiword Expressions (MWEs).
A Corpus of Text Data and Gaze Fixations from Autistic and Non-Autistic Adults
This division of the groups informs researchers on whether particular fixations were elicited from skillful or less-skillful readers and allows a fair between-group comparison for two levels of reading ability.
Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities
This paper presents an approach for automatic evaluation of the readability of text simplification output for readers with cognitive disabilities.
Diachronic Changes in Text Complexity in 20th Century English Language: An NLP Approach
In British English, we compared the complexity of texts published in 1931, 1961 and 1991, while in American English we compared the complexity of texts published in 1961 and 1992.
CLCM - A Linguistic Resource for Effective Simplification of Instructions in the Crisis Management Domain and its Evaluations
This article presents a new linguistic resource in the form of Controlled Language (CL) guidelines for manual text simplification in the CM domain which aims to address high TC in the CM domain and produce clear messages to be used in crisis situations.
A review corpus annotated for negation, speculation and their scope
This paper presents a freely available resource for research on handling negation and speculation in review texts.
