Haithem Afli
Lecturer+353 21 433 5529
haithem.afli[at]adaptcentre.ie
Publications
- From Arabic User-Generated Content to Machine Translation: Integrating Automatic Error Correction.
- A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis
- Sentiment Translation for low resourced languages: Experiments on Irish General Election Tweets
- Building and Using Multimodal Comparable Corpora for Machine Translation
- Maintaining sentiment polarity in translation of user-generated content
- Using SMT for OCR Error Correction of Historical Texts
- Identifying Effective Translations for Cross-lingual Arabic-to-English User-generated Speech Search
- Balancing Translation Quality and Sentiment Preservation
- FooTweets: A Bilingual Parallel Corpus of World Cup Tweets
- Finding Relevant Translations for Cross-lingual User-generated Speech Search
- A Systematic Comparison Between SMT and NMT on Translating User-Generated Content
- FaDA: Fast Document Aligner using Word Embedding
Profile
Dr Haithem Afli, based in Cork Institute of Technology (CIT), is lecturing Natural Language Processing (NLP), Machine Learning (ML) and Programming for Data Analytics at CIT. He is responsible for managing the ADAPT@CIT research group. After completing his undergraduate degree in Computer Science, he obtained an MSc in Computational Linguistics from Grenoble University in 2010 and a PhD in Computer Science from Le Mans University in 2014. His research interests are primarily in the areas of Social Media and User-Generated Content Analysis, Natural Language Processing, Machine Translation, Image and video caption generation and Deep Learning. He is motivated by the way humans deal with information transmitted in different modalities - texts, images and videos. In CIT Dr Afli is involved in supervising PhD and MSc students and coordinating the online MSc in Artificial Intelligence in CIT. As an academic researcher, he is keen to commercialise his research with industry partnerships and he is actively involved in managing academia-industry partnership projects.