Mark Lee

Also published as: M.G. Lee, Mark G. Lee


2021

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Kawarith: an Arabic Twitter Corpus for Crisis Events
Alaa Alharbi | Mark Lee
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Social media (SM) platforms such as Twitter provide large quantities of real-time data that can be leveraged during mass emergencies. Developing tools to support crisis-affected communities requires available datasets, which often do not exist for low resource languages. This paper introduces Kawarith a multi-dialect Arabic Twitter corpus for crisis events, comprising more than a million Arabic tweets collected during 22 crises that occurred between 2018 and 2020 and involved several types of hazard. Exploration of this content revealed the most discussed topics and information types, and the paper presents a labelled dataset from seven emergency events that serves as a gold standard for several tasks in crisis informatics research. Using annotated data from the same event, a BERT model is fine-tuned to classify tweets into different categories in the multi- label setting. Results show that BERT-based models yield good performance on this task even with small amounts of task-specific training data.

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Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis
Abdullah I. Alharbi | Mark Lee
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Sarcasm detection and sentiment analysis are important tasks in Natural Language Understanding. Sarcasm is a type of expression where the sentiment polarity is flipped by an interfering factor. In this study, we exploited this relationship to enhance both tasks by proposing a multi-task learning approach using a combination of static and contextualised embeddings. Our proposed system achieved the best result in the sarcasm detection subtask.

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UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation
Frances Adriana Laureano De Leon | Harish Tayyar Madabushi | Mark Lee
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This paper describes the participation of the UoB-NLP team in the ProfNER-ST shared subtask 7a. The task was aimed at detecting the mention of professions in social media text. Our team experimented with two methods of improving the performance of pre-trained models: Specifically, we experimented with data augmentation through translation and the merging of multiple language inputs to meet the objective of the task. While the best performing model on the test data consisted of mBERT fine-tuned on augmented data using back-translation, the improvement is minor possibly because multi-lingual pre-trained models such as mBERT already have access to the kind of information provided through back-translation and bilingual data.

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Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability
Venelin Kovatchev | Phillip Smith | Mark Lee | Rory Devine
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires (or “mindreading”). We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children’s performance on tests of mindreading, consisting of 10,320 question-answer pairs. We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.

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Extractive Financial Narrative Summarisation using SentenceBERT Based Clustering
Tuba Gokhan | Phillip Smith | Mark Lee
Proceedings of the 3rd Financial Narrative Processing Workshop

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UoB_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.
Wei Li | Harish Tayyar Madabushi | Mark Lee
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes our submission to SemEval 2021 Task 2. We compare XLM-RoBERTa Base and Large in the few-shot and zero-shot settings and additionally test the effectiveness of using a k-nearest neighbors classifier in the few-shot setting instead of the more traditional multi-layered perceptron. Our experiments on both the multi-lingual and cross-lingual data show that XLM-RoBERTa Large, unlike the Base version, seems to be able to more effectively transfer learning in a few-shot setting and that the k-nearest neighbors classifier is indeed a more powerful classifier than a multi-layered perceptron when used in few-shot learning.

2020

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Combining Character and Word Embeddings for the Detection of Offensive Language in Arabic
Abdullah I. Alharbi | Mark Lee
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection

Twitter and other social media platforms offer users the chance to share their ideas via short posts. While the easy exchange of ideas has value, these microblogs can be leveraged by people who want to share hatred. and such individuals can share negative views about an individual, race, or group with millions of people at the click of a button. There is thus an urgent need to establish a method that can automatically identify hate speech and offensive language. To contribute to this development, during the OSACT4 workshop, a shared task was undertaken to detect offensive language in Arabic. A key challenge was the uniqueness of the language used on social media, prompting the out-of-vocabulary (OOV) problem. In addition, the use of different dialects in Arabic exacerbates this problem. To deal with the issues associated with OOV, we generated a character-level embeddings model, which was trained on a massive data collected carefully. This level of embeddings can work effectively in resolving the problem of OOV words through its ability to learn the vectors of character n-grams or parts of words. The proposed systems were ranked 7th and 8th for Subtasks A and B, respectively.

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BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media
Abdullah I. Alharbi | Mark Lee
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Social media platforms such as Twitter offer people an opportunity to publish short posts in which they can share their opinions and perspectives. While these applications can be valuable, they can also be exploited to promote negative opinions, insults, and hatred against a person, race, or group. These opinions can be spread to millions of people at the click of a mouse. As such, there is a need to develop mechanisms by which offensive language can be automatically detected in social media channels and managed in a timely manner. To help achieve this goal, SemEval 2020 offered a shared task (OffensEval 2020) that involved the detection of offensive text in Arabic. We propose an ensemble approach that combines different levels of word embedding models and transfers learning from other sources of emotion-related tasks. The proposed system ranked 9th out of the 52 entries within the Arabic Offensive language identification subtask.

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“What is on your mind?” Automated Scoring of Mindreading in Childhood and Early Adolescence
Venelin Kovatchev | Phillip Smith | Mark Lee | Imogen Grumley Traynor | Irene Luque Aguilera | Rory Devine
Proceedings of the 28th International Conference on Computational Linguistics

In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence. We create MIND-CA, a new corpus of 11,311 question-answer pairs in English from 1,066 children aged from 7 to 14. We perform machine learning experiments and carry out extensive quantitative and qualitative evaluation. We obtain promising results, demonstrating the applicability of state-of-the-art NLP solutions to a new domain and task.

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Augmenting Neural Metaphor Detection with Concreteness
Ghadi Alnafesah | Harish Tayyar Madabushi | Mark Lee
Proceedings of the Second Workshop on Figurative Language Processing

The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while. However, recent methods of detecting metaphor that have relied on deep neural models have ignored concreteness and related psycholinguistic information. We hypothesis that this information is not available to these models and that their addition will boost the performance of these models in detecting metaphor. We test this hypothesis on the Metaphor Detection Shared Task 2020 and find that the addition of concreteness information does in fact boost deep neural models. We also run tests on data from a previous shared task and show similar results.

2019

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Crisis Detection from Arabic Tweets
Alaa Alharbi | Mark Lee
Proceedings of the 3rd Workshop on Arabic Corpus Linguistics

2018

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Integrating Question Classification and Deep Learning for improved Answer Selection
Harish Tayyar Madabushi | Mark Lee | John Barnden
Proceedings of the 27th International Conference on Computational Linguistics

We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.

2016

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UoB-UK at SemEval-2016 Task 1: A Flexible and Extendable System for Semantic Text Similarity using Types, Surprise and Phrase Linking
Harish Tayyar Madabushi | Mark Buhagiar | Mark Lee
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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High Accuracy Rule-based Question Classification using Question Syntax and Semantics
Harish Tayyar Madabushi | Mark Lee
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We present in this paper a purely rule-based system for Question Classification which we divide into two parts: The first is the extraction of relevant words from a question by use of its structure, and the second is the classification of questions based on rules that associate these words to Concepts. We achieve an accuracy of 97.2%, close to a 6 point improvement over the previous State of the Art of 91.6%. Additionally, we believe that machine learning algorithms can be applied on top of this method to further improve accuracy.

2015

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Sentiment Classification via a Response Recalibration Framework
Phillip Smith | Mark Lee
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2014

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A Hybrid Approach to Features Representation for Fine-grained Arabic Named Entity Recognition
Fahd Alotaibi | Mark Lee
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Automatically Developing a Fine-grained Arabic Named Entity Corpus and Gazetteer by utilizing Wikipedia
Fahd Alotaibi | Mark Lee
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Building Text-to-Speech Systems for Resource Poor Languages
Nur-Hana Samsudin | Mark Lee
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper describes research on building text-to-speech synthesis systems (TTS) for resource poor languages using available resources from other languages and describes our general approach to building cross-linguistic polyglot TTS. Our approach involves three main steps: language clustering, grapheme to phoneme mapping and prosody modelling. We have tested the mapping of phonemes from German to English and from Indonesian to Spanish. We have also constructed three prosody representations for different language characteristics. For evaluation we have developed an English TTS based on German data, and a Spanish TTS based on Indonesian data and compared their performance against pre-existing monolingual TTSs. Since our motivation is to develop speech synthesis for resource poor languages, we have also developed three TTS for Iban, an Austronesian language with practically no available language resources, using Malay, Indonesian and Spanish resources.

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Cross-discourse Development of Supervised Sentiment Analysis in the Clinical Domain
Phillip Smith | Mark Lee
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis

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A CCG-based Approach to Fine-Grained Sentiment Analysis
Phillip Smith | Mark Lee
Proceedings of the 2nd Workshop on Sentiment Analysis where AI meets Psychology

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Mapping Arabic Wikipedia into the Named Entities Taxonomy
Fahd Alotaibi | Mark Lee
Proceedings of COLING 2012: Posters

2008

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Textual Entailment as an Evaluation Framework for Metaphor Resolution: A Proposal
Rodrigo Agerri | John Barnden | Mark Lee | Alan Wallington
Semantics in Text Processing. STEP 2008 Conference Proceedings

2007

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Don’t worry about metaphor: affect detection for conversational agents
Catherine Smith | Timothy Rumbell | John Barnden | Robert Hendley | Mark Lee | Alan Wallington | Li Zhang
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

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On the formalization of Invariant Mappings for Metaphor Interpretation
Rodrigo Agerri | John Barnden | Mark Lee | Alan Wallington
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

2006

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Considerations on the nature of metaphorical meaning arising from a computational treatment of metaphor interpretation
A.M. Wallington | R. Agerri | J.A. Barnden | S.R. Glasbey | M.G. Lee
Proceedings of the Fifth International Workshop on Inference in Computational Semantics (ICoS-5)

2003

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Domain-transcending mappings in a system for metaphorical reasoning
John A. Barnden | Sheila R. Glasbey | Mark G. Lee | Alan M. Wallington
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Reasoning in Metaphor Understanding: The ATT-Meta Approach and System
John Barnden | Sheila Glasbey | Mark Lee | Alan Wallington
COLING 2002: The 17th International Conference on Computational Linguistics: Project Notes

1996

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An ascription-based approach to Speech Acts
Mark Lee | Yorick Wilks
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics