Text Mining and Applications

The 10th Track of Text Mining and Applications (TeMA 2022) 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 IA 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 text mining methods, at multiple levels and in multiple dimensions, and often with high commercial value.


The topics of interest include, but are not limited to: 

  • TM, NLP, and Social Media Content Analysis
    • Entity Recognition and Disambiguation
    • Relation Extraction
    • Analysis of Opinions, Emotions and Sentiments
    • Text Clustering and Classification
    • Machine Translation
    • Summarization
    • Word Sense Disambiguation
    • Co-Reference Resolution
    • Language Modeling
    • Syntax and Parsing
    • Distributional Models and Semantics
    • Multi-Word Units
    • Lexical Knowledge Acquisition
    • Spatio-Temporal Text Mining
    • Entailment and Paraphrases
    • Natural Language Generation
    • Language Resources: Acquisition and Usage
    • Cross-Lingual Approaches
    • Algorithms and Data Structures for Text Mining
  • Applications
    • Information Retrieval and Information Extraction
    • Question-Answering and Dialogue Systems
    • Text-Based Prediction and Forecasting
    • Web Content Annotation
    • Computational Social Science
    • Computational Journalism
    • Health and Well-being
    • Big Data Analysis

Organisation Committee

  • Joaquim Silva, FCT/UNL, Portugal
  • Pablo Gamallo, Universidade de Santiago de Compostela, Spain
  • Paulo Quaresma, Uviversidade de Évora, Portugal
  • Irene Rodrigues, Universidade de Évora, Portugal

Program Committee

  • Adam Jatowt – Universit of Kioto, Japan
  • Alberto Simões – Algoritmi Center – University of Minho, Portugal
  • Alexandre Rademaker – IBM / FGV, Brazil
  • Antoine Doucet – University of Caen, France
  • António Branco – Universidade de Lisboa, Portugal
  • Béatrice Daille – University of Nantes, France
  • Bruno Martins – Instituto Superior Técnico – Universidade de Lisboa, Portugal
  • Fernando Batista – Instituto Universitário de Lisboa, Portugal
  • Francisco Couto – Faculdade de Ciências – Universidade de Lisboa, Portugal
  • Gaël Dias – University of Caen Basse-Normandie, France
  • Hugo Oliveira – Universidade de Coimbra, Portugal
  • Iñaki San Vicente Roncal, Elhuyar Fundazioa, Spain
  • Irene Rodrigues – Universidade de Évora, Portugal
  • Jesús Vilares – University of A Coruña, Spain
  • Joaquim Ferreira da Silva – Faculdade de Ciências e Tecnologia – Universidade Nova de Lisboa
  • Luisa Coheur – Universidade Técnica de Lisboa, Portugal
  • Manuel Vilares Ferro – University of Vigo, Spain
  • Marcos Garcia, University of Santiago de Compostela, Spain
  • Miguel A. Alonso, University of A Coruña, Spain
  • Nuno Mamede – Universidade Técnica de Lisboa, Portugal
  • Pablo Gamallo – Faculdade de Filologia, Santiago de Compustela, Spain
  • Paulo Quaresma – Universidade de Évora, Portugal
  • Pavel Brazdil – University of Porto, Portugal
  • Sérgio Nunes – Faculdade de Engenharia – Universidade do Porto, Portugal
  • Renata Vieira – Universidade de Évora, Portugal