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.
no code implementations • CONLL 2017 • Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Marco A. Valenzuela-Esc{\'a}rcega, Peter Clark, Michael Hammond
We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection.
Ranked #1 on Question Answering on AI2 Kaggle Dataset
no code implementations • WS 2018 • Fan Luo, Marco A. Valenzuela-Esc{\'a}rcega, Gus Hahn-Powell, Mihai Surdeanu
We introduce a machine learning approach for the identification of {``}white spaces{''} in scientific knowledge.
1 code implementation • NAACL 2019 • Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Esc{\'a}rcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
no code implementations • NAACL 2019 • George C. G. Barbosa, Zechy Wong, Gus Hahn-Powell, Dane Bell, Rebecca Sharp, Marco A. Valenzuela-Esc{\'a}rcega, Mihai Surdeanu
Many of the most pressing current research problems (e. g., public health, food security, or climate change) require multi-disciplinary collaborations.
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.
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.
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.
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).