2021 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to natural language processing and computational social science.
2019 - ACM Fellow For contributions to natural language processing, with innovations in data-driven and graph-based language processing
Her main research concerns Artificial intelligence, Natural language processing, Information retrieval, SemEval and Word-sense disambiguation. Artificial intelligence is frequently linked to Subjectivity in her study. Her study in Natural language processing is interdisciplinary in nature, drawing from both Graph, Word and Computational model.
Her Information retrieval course of study focuses on Natural language and Text processing, Open text, PageRank and Semantic network. Her SemEval study incorporates themes from Sentence and Construct. Rada Mihalcea usually deals with Word-sense disambiguation and limits it to topics linked to Pattern recognition and Graph database and Computational linguistics.
Her primary areas of study are Artificial intelligence, Natural language processing, Information retrieval, Word and Word-sense disambiguation. Much of her study explores Artificial intelligence relationship to Context. In most of her Context studies, her work intersects topics such as Utterance.
Her studies link Subjectivity with Natural language processing. Her Information retrieval study integrates concerns from other disciplines, such as Annotation and World Wide Web. Her Question answering study frequently draws connections between related disciplines such as Information extraction.
Her main research concerns Artificial intelligence, Natural language processing, Human–computer interaction, Utterance and Context. Her Artificial intelligence research includes elements of Variety, Graph and Style. Her work in Natural language processing addresses issues such as Leverage, which are connected to fields such as Noun.
Her biological study spans a wide range of topics, including Distracted driving, User-generated content, Deception and Modalities. Rada Mihalcea has included themes like Tone, Syllable, Expression and Face in her Utterance study. Her research integrates issues of Crowdsourcing, Conversation, Dialog box and Perception in her study of Context.
Her scientific interests lie mostly in Natural language processing, Artificial intelligence, Conversation, Utterance and Context. Her primary area of study in Natural language processing is in the field of Sentiment analysis. Many of her research projects under Artificial intelligence are closely connected to Family history with Family history, tying the diverse disciplines of science together.
Her studies in Conversation integrate themes in fields like Emotion recognition, Field, Frustration, Key and Data science. The concepts of her Utterance study are interwoven with issues in Code, Cognitive science and Benchmark. Her Context study deals with Dialog box intersecting with Empirical research, Classifier, Word error rate and Identification.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
TextRank: Bringing Order into Text
Rada Mihalcea;Paul Tarau.
empirical methods in natural language processing (2004)
Corpus-based and knowledge-based measures of text semantic similarity
Rada Mihalcea;Courtney Corley;Carlo Strapparava.
national conference on artificial intelligence (2006)
Wikify!: linking documents to encyclopedic knowledge
Rada Mihalcea;Andras Csomai.
conference on information and knowledge management (2007)
SemEval-2007 Task 14: Affective Text
Carlo Strapparava;Rada Mihalcea.
meeting of the association for computational linguistics (2007)
Learning to identify emotions in text
Carlo Strapparava;Rada Mihalcea.
acm symposium on applied computing (2008)
Graph-based ranking algorithms for sentence extraction, applied to text summarization
Rada Mihalcea.
meeting of the association for computational linguistics (2004)
Learning Multilingual Subjective Language via Cross-Lingual Projections
Rada Mihalcea;Carmen Banea;Janyce Wiebe.
meeting of the association for computational linguistics (2007)
Measuring the Semantic Similarity of Texts
Courtney Corley;Rada Mihalcea.
meeting of the association for computational linguistics (2005)
SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability
Eneko Agirre;Carmen Banea;Claire Cardie;Daniel Cer.
north american chapter of the association for computational linguistics (2015)
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation
Eneko Agirre;Carmen Banea;Daniel M. Cer;Mona T. Diab.
north american chapter of the association for computational linguistics (2016)
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