A Statistical Machine Learning Approach to Generating Graph Structures from Food Recipes

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dc.contributor.advisor Pustejovsky, James en_US
dc.contributor.author Chen, Yuzhe
dc.date.accessioned 2017-09-04T13:07:24Z
dc.date.available 2017-09-04T13:07:24Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/10192/34135
dc.description.abstract Food recipes, as a type of instructional text, contain series of instructions that guide human users through cooking processes. Their structures can usually be represented as flow graphs: at the start state, a collection of ingredients are introduced. Then, a series of actions are performed on these ingredients (along with cooking tools). Finally, the completed dish is produced at the end. A computer system that can extract such structures from food recipes can have many useful applications. Since food recipes are written by humans, they pose many of the same problems the field of Natural Language Processing is trying to solve. Furthermore, they also have challenges that are not commonly seen in many other types of texts. This thesis explores a Statistical Machine Learning approach to food recipe text processing. I will detail my work in the creation of an annotated food recipe dataset used in supervised learning. I will also describe my work in training Machine Learning algorithms that perform Named Entity Recognition and Relation Extraction tasks on this dataset. en_US
dc.description.sponsorship Brandeis University, Graduate School of Arts and Sciences en_US
dc.format.mimetype application/pdf en_US
dc.language English en_US
dc.language.iso eng en_US
dc.publisher Brandeis University en_US
dc.rights Copyright by Yuzhe Chen 2017 en_US
dc.subject recipe en_US
dc.subject food en_US
dc.subject graph en_US
dc.subject NLP en_US
dc.subject natural language processing en_US
dc.subject graph structures en_US
dc.subject text processing en_US
dc.subject information extraction en_US
dc.subject machine learning en_US
dc.title A Statistical Machine Learning Approach to Generating Graph Structures from Food Recipes en_US
dc.type Thesis en_US
dc.contributor.department Graduate Program in Computational Linguistics en_US
dc.degree.name MA en_US
dc.degree.level Masters en_US
dc.degree.discipline Computational Linguistics en_US
dc.degree.grantor Brandeis University, Graduate School of Arts and Sciences en_US


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