Search Results for author: Marco A. Valenzuela-Esc{\'a}rcega

Found 11 papers, 1 papers with code

An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification

no code implementations COLING 2020 Robert Vacareanu, Marco A. Valenzuela-Esc{\'a}rcega, Rebecca Sharp, Mihai Surdeanu

This paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic relation between the compound constituents (e. g. the label employer is associated with the noun compound government agency) (Tratz, 2011).

Odinson: A Fast Rule-based Information Extraction Framework

no code implementations LREC 2020 Marco A. Valenzuela-Esc{\'a}rcega, Gus Hahn-Powell, Dane Bell

We present Odinson, a rule-based information extraction framework, which couples a simple yet powerful pattern language that can operate over multiple representations of text, with a runtime system that operates in near real time.

MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions

no code implementations LREC 2020 Maria Alexeeva, Rebecca Sharp, Marco A. Valenzuela-Esc{\'a}rcega, Jennifer Kadowaki, Adarsh Pyarelal, Clayton Morrison

Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired.

Information Retrieval Reading Comprehension +1

Parsing as Tagging

no code implementations LREC 2020 Robert Vacareanu, George Caique Gouveia Barbosa, Marco A. Valenzuela-Esc{\'a}rcega, Mihai Surdeanu

For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left.

Dependency Parsing Position +2

Odin's Runes: A Rule Language for Information Extraction

no code implementations LREC 2016 Marco A. Valenzuela-Esc{\'a}rcega, Gus Hahn-Powell, Mihai Surdeanu

Odin is an information extraction framework that applies cascades of finite state automata over both surface text and syntactic dependency graphs.

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