================== Special issue summary ================== There is an abundance of text data available in a variety of domains. These data offer a large potential for knowledge discovery if the texts can be effectively disclosed with data mining techniques. However, text data is challenging for data mining because it is typically unstructured, often noisy, and open-ended – newly added documents bring new vocabulary and thus new features.
In developing Text Mining methods, every domain has its own unique challenges. Examples of complex text types that have gained attention of researchers in the past decade are: scientific publications, historic documents, patents, electronic health records, policy documents, and social media data. Text Mining research has its roots in the Natural Language Processing community as well as the Information Retrieval community, and receives attention from many application domains. We are seeking for more coherence in Text Mining research, by bringing together papers on text mining research from different angles.
This special issue invites submissions on the following topics: • Text mining methods, among which: named entity recognition, relation extraction, text categorization, text summarization, authorship detection, sentiment analysis • Text mining applications for complex domains • Domain adaptation for text mining methods • Evaluation of text mining methods • Pre-processing pipelines for text mining • Natural Language Processing for text mining • Information Retrieval for text mining • User interfacing for text mining • User studies addressing text mining applications • Methods for mining text with images
================= Manuscript submission ================= All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page: https://www.mdpi.com/journal/mti/instructions
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