2020 - Member of Academia Europaea
2019 - Royal Society of Edinburgh
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Automatic summarization, Parsing and Meaning. Her Artificial intelligence research incorporates themes from Process and State. Mirella Lapata interconnects Machine learning and Information retrieval in the investigation of issues within Natural language processing.
The various areas that Mirella Lapata examines in her Machine learning study include Graph, Connectivity, Graph, Range and Semantics. Mirella Lapata has researched Automatic summarization in several fields, including Algorithm, Optimization problem and Theoretical computer science. Her Parsing research is multidisciplinary, relying on both Chunking, Chunking, Head-driven phrase structure grammar and Identification.
Her primary scientific interests are in Artificial intelligence, Natural language processing, Automatic summarization, Parsing and Sentence. Her Artificial intelligence research includes themes of Machine learning, Information retrieval and Selection. Her Natural language processing research integrates issues from Context, Word and Meaning.
Her research in the fields of Multi-document summarization overlaps with other disciplines such as Encoder. Within one scientific family, Mirella Lapata focuses on topics pertaining to Dependency under Parsing, and may sometimes address concerns connected to Syntax. Her research investigates the connection between Sentence and topics such as Artificial neural network that intersect with problems in State.
Artificial intelligence, Natural language processing, Automatic summarization, Transformer and Parsing are her primary areas of study. Her work in Artificial intelligence tackles topics such as Machine learning which are related to areas like Text generation. The Natural language processing study combines topics in areas such as Narrative structure and German.
Her Automatic summarization research is multidisciplinary, incorporating elements of Language model, Salient and Training set. As part of one scientific family, Mirella Lapata deals mainly with the area of Transformer, narrowing it down to issues related to the Benchmark, and often Cluster analysis, Interpretation and Space. The concepts of her Parsing study are interwoven with issues in Recurrent neural network, Theoretical computer science, Representation, Utterance and Semantic role labeling.
Her scientific interests lie mostly in Artificial intelligence, Natural language processing, Automatic summarization, Artificial neural network and Sentence. In her works, Mirella Lapata conducts interdisciplinary research on Artificial intelligence and Content. Her Parsing study in the realm of Natural language processing connects with subjects such as Screenwriting.
Her Automatic summarization research also works with subjects such as
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.
Composition in distributional models of semantics.
Jeff Mitchell;Mirella Lapata.
Cognitive Science (2010)
Dependency-Based Construction of Semantic Space Models
Sebastian Padó;Mirella Lapata.
Computational Linguistics (2007)
Vector-based Models of Semantic Composition
Jeff Mitchell;Mirella Lapata.
meeting of the association for computational linguistics (2008)
Modeling local coherence: An entity-based approach
Regina Barzilay;Regina Barzilay;Mirella Lapata.
Computational Linguistics (2008)
Using the web to obtain frequencies for unseen bigrams
Frank Keller;Mirella Lapata.
Computational Linguistics (2003)
Using Semantic Roles to Improve Question Answering
Dan Shen;Mirella Lapata.
empirical methods in natural language processing (2007)
Long Short-Term Memory-Networks for Machine Reading
Jianpeng Cheng;Li Dong;Mirella Lapata.
empirical methods in natural language processing (2016)
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
R. Navigli;M. Lapata.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
Probabilistic Text Structuring: Experiments with Sentence Ordering
meeting of the association for computational linguistics (2003)
Global inference for sentence compression an integer linear programming approach
James Clarke;Mirella Lapata.
Journal of Artificial Intelligence Research (2008)
Profile was last updated on December 6th, 2021.
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