What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Genetics
Martin Krallinger mostly deals with Information retrieval, Text mining, Annotation, Data science and Biomedical text mining.
His study on Information retrieval is mostly dedicated to connecting different topics, such as Named-entity recognition.
His Text mining research is multidisciplinary, relying on both Computational linguistics, Molecular biology and Information science.
His study in Annotation is interdisciplinary in nature, drawing from both Manual curation, Text corpus, Natural language processing and Protein–protein interaction.
The study incorporates disciplines such as Biological database and Information extraction in addition to Data science.
His research on Biomedical text mining also deals with topics like
- World Wide Web which is related to area like Workflow,
- Natural language and related The Internet, Biological data and Bioinformatics.
His most cited work include:
- Overview of the protein-protein interaction annotation extraction task of BioCreative II (241 citations)
- The CHEMDNER corpus of chemicals and drugs and its annotation principles. (220 citations)
- Linking genes to literature: text mining, information extraction, and retrieval applications for biology (196 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Information retrieval, Text mining, Artificial intelligence, Natural language processing and Information extraction.
His biological study spans a wide range of topics, including Annotation and Interoperability.
Martin Krallinger has included themes like Named-entity recognition, World Wide Web, Data science and Bioinformatics in his Text mining study.
His Data science research is multidisciplinary, relying on both Biological database and Workflow.
The Natural language processing study combines topics in areas such as Data mining and Protein–protein interaction.
Martin Krallinger combines subjects such as The Internet and Biological data with his study of Information extraction.
He most often published in these fields:
- Information retrieval (38.27%)
- Text mining (33.33%)
- Artificial intelligence (32.10%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (32.10%)
- Natural language processing (29.63%)
- Information retrieval (38.27%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Natural language processing, Information retrieval, Search engine indexing and Named-entity recognition are his primary areas of study.
His Natural language processing research is multidisciplinary, incorporating elements of Embedding and De-identification.
His study of Ontology is a part of Information retrieval.
His Named-entity recognition study combines topics in areas such as Text mining, Field, Relevance and Identification.
His studies in Text mining integrate themes in fields like Normalization and Data processing.
The concepts of his Relevance study are interwoven with issues in Annotation and Biomedical text mining.
Between 2018 and 2021, his most popular works were:
- Medical Word Embeddings for Spanish: Development and Evaluation (32 citations)
- PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track (28 citations)
- Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results. (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- Natural language processing
Martin Krallinger spends much of his time researching Artificial intelligence, Natural language processing, Named-entity recognition, Text mining and Relevance.
The Training set, Machine translation and De-identification research Martin Krallinger does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Portuguese and Track, therefore creating a link between diverse domains of science.
Martin Krallinger has researched Training set in several fields, including Unified Medical Language System and Transformer.
Portuguese and Task are two areas of study in which Martin Krallinger engages in interdisciplinary work.
His work on Biomedical text mining as part of general Text mining study is frequently linked to Coding, bridging the gap between disciplines.
His research in Relevance intersects with topics in Embedding and Word, Word2vec.
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