Natural Language Processing

The increasing volume of digital textual information is calling for efficient and effective Natural Language Processing (NLP) methods that will allow humans and machines alike to digest the continuous flow of all the generated information. NLP brings together the fields of Artificial Intelligence (AI), Computer Science and Linguistics to enable computers to process and interpret human language in a way that mimics human understanding. NLP has been instrumental in providing powerful and accurate methods for various tasks ranging from named entity recognition, part-of-speech tagging and sentiment analysis to more complex tasks such as text summarization, conversational systems and intelligent agents. In addition, the latest technological advancements in Large Language Models (LLMs) have made it possible to develop and deploy even more powerful NLP systems that incorporate semantic and contextual knowledge, bringing them even closer to human-level performance.

M4D has actively participated in many NLP-related projects and has worked towards developing efficient and accurate NLP methods and tools. Key research and development activities of M4D in the field of NLP include:

  • Conversational Systems / Intelligent Agents / Chatbots
  • Named Entity Recognition / Relation Extraction in various domains
  • Sentiment Analysis / Emotion Classification/ Text Classification
  • Speech Processing / Language Identification / Disfluency Detection and Identification
  • Discourse Analysis / Discourse Parsing / Coreference Resolution
Medical-focused relation extraction service user interface: query and results on TrAP relation
Multilingual Named Entity Recognition service user interface, focusing on location mentions – Query field
Multilingual Named Entity Recognition service user interface, focusing on location mentions – Results

PUBLICATIONS

  1. N. Mylonas et al, 'Online Child Grooming Detection: Challenges and Future Directions". In: Gkotsis, I., Kavallieros, D., Stoianov, N., Vrochidis, S., Diagourtas, D., Akhgar, B. (eds) Paradigms on Technology Development for Security Practitioners. Security Informatics and Law Enforcement. Springer, Cham, pp 237–247. https://doi.org/10.1007/978-3-031-62083-6_19