Artificial intelligence, Natural language processing, Information retrieval, Question answering and WordNet are her primary areas of study. Sanda M. Harabagiu works mostly in the field of Artificial intelligence, limiting it down to topics relating to Relation and, in certain cases, Textual entailment and Filter. Her study in Natural language processing is interdisciplinary in nature, drawing from both Negation and Contradiction.
Her work on Automatic summarization as part of her general Information retrieval study is frequently connected to Net, thereby bridging the divide between different branches of science. Her Question answering research incorporates themes from Context and Semantics. In the field of WordNet, her study on eXtended WordNet overlaps with subjects such as Resource and Falcon.
Sanda M. Harabagiu mainly focuses on Artificial intelligence, Natural language processing, Information retrieval, Question answering and WordNet. Her study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning. Her research links Semantics with Natural language processing.
In general Information retrieval, her work in Automatic summarization, Ranking, Unified Medical Language System and Relevance is often linked to Medical record linking many areas of study. Her research investigates the connection between Question answering and topics such as Inference that intersect with issues in Knowledge-based systems. The concepts of her Information extraction study are interwoven with issues in Relation, Knowledge base and Knowledge acquisition.
Sanda M. Harabagiu spends much of her time researching Artificial intelligence, Natural language processing, Deep learning, Information retrieval and Electroencephalography. Sanda M. Harabagiu combines subjects such as Machine learning, Computer vision and Identification with her study of Artificial intelligence. Her Natural language processing research incorporates elements of Dependency, Semantics and Knowledge graph.
Her Deep learning research is multidisciplinary, relying on both Clinical trial and Negation. She interconnects Probabilistic logic, Data science and Knowledge representation and reasoning in the investigation of issues within Information retrieval. Her Probabilistic logic study incorporates themes from Question answering, Clinical decision support system, Inference and Medical algorithm.
Sanda M. Harabagiu focuses on Artificial intelligence, Deep learning, Electroencephalography, Natural language processing and Medical record. Many of her studies on Artificial intelligence apply to Computer vision as well. Her work deals with themes such as Ontology, Relevance and Identification, which intersect with Deep learning.
She focuses mostly in the field of Natural language processing, narrowing it down to topics relating to Variety and, in certain cases, Data mining, Annotation and Modality. Her study looks at the relationship between Probabilistic logic and topics such as Information retrieval, which overlap with Knowledge representation and reasoning. Her work on Question answering is being expanded to include thematically relevant topics such as Data science.
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Performance issues and error analysis in an open-domain question answering system
Dan Moldovan;Marius Paşca;Sanda Harabagiu;Mihai Surdeanu.
ACM Transactions on Information Systems (2003)
Using Predicate-Argument Structures for Information Extraction
Mihai Surdeanu;Sanda Harabagiu;John Williams;Paul Aarseth.
meeting of the association for computational linguistics (2003)
FALCON: Boosting Knowledge for Answer Engines
Sanda M. Harabagiu;Dan I. Moldovan;Marius. Paşca;Rada Mihalcea.
text retrieval conference (2000)
LASSO: A Tool for Surfing the Answer Net
Dan I. Moldovan;Sanda M. Harabagiu;Marius. Paşca;Rada Mihalcea.
text retrieval conference (1999)
Experiments with open-domain textual Question Answering
Sanda M. Harabagiu;Marius A. Paşca;Steven J. Maiorano.
international conference on computational linguistics (2000)
Question answering based on semantic structures
Srini Narayanan;Sanda Harabagiu.
international conference on computational linguistics (2004)
Methods for Using Textual Entailment in Open-Domain Question Answering
Sanda Harabagiu;Andrew Hickl.
meeting of the association for computational linguistics (2006)
EmpaTweet: Annotating and Detecting Emotions on Twitter
Kirk Roberts;Michael A. Roach;Joseph Johnson;Josh Guthrie.
language resources and evaluation (2012)
LCC Tools for Question Answering.
Dan I. Moldovan;Sanda M. Harabagiu;Roxana Girju;Paul Morarescu.
text retrieval conference (2002)
The structure and performance of an open-domain question answering system
Dan Moldovan;Sanda Harabagiu;Marius Pasca;Rada Mihalcea.
meeting of the association for computational linguistics (2000)
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