Introduction

The Track of Natural Language, Text Mining and Applications (NLP-TeMA 2025) is a forum for researchers working in Human Language Technologies, i.e. Natural Language Processing (NLP), Computational Linguistics (CL), Natural Language Engineering (NLE), Text Mining (TM), Information Retrieval (IR), and related areas.
The most natural form of sharing knowledge is indeed through textual documents. Especially on the Web, a huge amount of textual information is openly published every day, on many different topics and written in natural language, thus offering new insights and many opportunities for innovative applications of Human Language Technologies.
Following advances in general AI sub-fields such as NLP, Machine Learning (ML) and Deep Learning (DL), text mining is now even more valuable as tool for bridging the gap between language theories and effective use of natural language contents, for harnessing the power of semi-structured and unstructured data, and to enable important applications in real-world heterogeneous environments. Both hidden and new knowledge can be discovered by using NLP and Text Mining methods, at multiple levels and in multiple dimensions, and often with high commercial value.

Topics of interest

Theories, Algorithms and Models:

• Language and Cognitive Modeling
• Tagging, Chunking and Parsing
• Morphology and Word Segmentation
• Natural Language Generation
• Discourse and Pragmatics
• Semantics and Text Inference
• Language Resources: Acquisition and Usage. Lexical Knowledge Acquisition
• Entailment and Paraphrases
• Entity Recognition and Word Sense Disambiguation
• Natural language understanding
• Language modeling
• Mathematical Properties of Language
• NLP for Low-Resource Languages

Text Mining and NLP Applications:

• Text Clustering, Classification and Summarization
• Sentiment Analysis and Argument Mining
• Computational Social Science
• Multi-Word Units
• Machine Learning for NLP and Text Mining
• Spatio-Temporal and Big Text Mining
• Machine Translation and Cross-Lingual Approaches
• Algorithms and Data Structures for Text Mining
• Information Retrieval and Information Extraction
• Question-Answering and Dialogue Systems
• Text-Based Prediction and Forecasting
• Web Content Annotation
• Health/Biomedical/Legal and other Text Mining Applications
• Offensive Speech Detection and Analysis

Organizing committee

• Joaquim Silva, DI – FCT/UNL, Portugal
• Pablo Gamallo, Universidade de Santiago de Compostela, Spain
• Paulo Quaresma, Universidade de Évora, Portugal
• Irene Rodrigues, Universidade de Évora, Portugal
• Alípio Jorge, Universidade do Porto, Portugal