M
Maria Leonor Pacheco
Researcher at Purdue University
Publications - 17
Citations - 117
Maria Leonor Pacheco is an academic researcher from Purdue University. The author has contributed to research in topics: Context (language use) & Graph (abstract data type). The author has an hindex of 6, co-authored 17 publications receiving 73 citations.
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Journal ArticleDOI
Modeling Content and Context with Deep Relational Learning
TL;DR: DRaiL is presented, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios, and provides an interface to study the interactions between representation, inference and learning.
Journal ArticleDOI
Modeling Content and Context with Deep Relational Learning
TL;DR: DRaiL as discussed by the authors is an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios, and provides an interface to study the interactions between representation, inference and learning.
Random Forest with Increased Generalization: A Universal Background Approach for Authorship Verification Notebook for PAN at CLEF 2015
TL;DR: The approach uses Random Forest and a feature-encoding scheme based on the Universal Background Model strategy, building different feature vectors that describe the complete population of authors in a dataset, the known author, and the questioned document and combines the three of them in a single representation.
Proceedings ArticleDOI
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations
TL;DR: This paper proposes to represent this type of information as a narrative graph and learn contextualized event representations over it using a relational graph neural network model, and shows that the model can improve performance when learning script knowledge without direct supervision and provide a better representation for the implicit discourse sense classification task.
Journal ArticleDOI
Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols
TL;DR: In this paper, the authors study automated learning of protocol rules from textual specifications (i.e., RFCs) and apply them to a state-of-the-art fuzzer for transport protocols.