What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Natural language processing
- Programming language
Her scientific interests lie mostly in Artificial intelligence, Natural language processing, Annotation, Treebank and Verb.
Martha Palmer combines topics linked to Information retrieval with her work on Artificial intelligence.
Her research integrates issues of Speech recognition and SemEval in her study of Natural language processing.
The concepts of her Annotation study are interwoven with issues in Relational database and Representation.
Her Treebank study combines topics in areas such as Natural language understanding, Dependency grammar, Phrase and Lexical database.
Her Verb study combines topics from a wide range of disciplines, such as Lexical density, WordNet, Lexicon, Polysemy and Syntax.
Her most cited work include:
- VERB SEMANTICS AND LEXICAL SELECTION (2500 citations)
- The Proposition Bank: An Annotated Corpus of Semantic Roles (1883 citations)
- Abstract Meaning Representation for Sembanking (747 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Artificial intelligence, Natural language processing, Verb, Annotation and Semantic role labeling.
Her study in PropBank, VerbNet, Semantics, Lexicon and Natural language is carried out as part of her Artificial intelligence studies.
Her PropBank research incorporates elements of Light verb and Proposition.
Martha Palmer frequently studies issues relating to Information retrieval and Natural language processing.
Her study in Verb is interdisciplinary in nature, drawing from both Sentence, WordNet, Polysemy and Cluster analysis.
The Syntax study combines topics in areas such as Grammar and Rule-based machine translation.
She most often published in these fields:
- Artificial intelligence (77.67%)
- Natural language processing (74.21%)
- Verb (24.53%)
What were the highlights of her more recent work (between 2014-2021)?
- Artificial intelligence (77.67%)
- Natural language processing (74.21%)
- Annotation (24.21%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Artificial intelligence, Natural language processing, Annotation, Verb and PropBank.
The concepts of her Artificial intelligence study are interwoven with issues in Domain and Identification.
Her Natural language processing research is multidisciplinary, relying on both Semantics, Information retrieval and Coreference.
Her biological study spans a wide range of topics, including Leverage, Relation, Schema, Data science and Conceptualization.
Her Verb research integrates issues from Topic model, Speech recognition, Cluster analysis, Adjective and Lexicon.
Martha Palmer combines subjects such as Proposition, Classifier, WordNet, Unification and Predicate with her study of PropBank.
Between 2014 and 2021, her most popular works were:
- Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation (65 citations)
- SemEval-2017 Task 12: Clinical TempEval (64 citations)
- Identifying and Categorizing Disaster-Related Tweets. (41 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Natural language processing
- Programming language
Martha Palmer mainly investigates Artificial intelligence, Natural language processing, Annotation, Coreference and Information retrieval.
When carried out as part of a general Artificial intelligence research project, her work on Deep learning and SemEval is frequently linked to work in Natural disaster, Geospatial analysis and Timeline, therefore connecting diverse disciplines of study.
Her work on Treebank, Parsing and PropBank as part of general Natural language processing research is frequently linked to Empirical research, thereby connecting diverse disciplines of science.
Her Annotation research is multidisciplinary, incorporating elements of Scheme, Variety and Social media.
Her Coreference research incorporates themes from Semantics and Information extraction.
Her studies in Information retrieval integrate themes in fields like Domain, FrameNet and Manual annotation.
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