Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Marco Antonio Valenzuela Escárcega"'
Autor:
Gastón R. Torrescano Urrutia, Armida Sánchez Escalantea, Martín Gustavo Vásquez Palma, Marco Antonio Valenzuela Escárcega, Ricardo Paz Pellat, Dino A. Pardo Guzmán
Publikováno v:
Revista Mexicana de Ciencias Pecuarias, Vol 3, Iss 3, Pp 299-312 (2012)
Actualmente, el análisis de clasificación de calidad de la canal depende en gran medida de la edad del animal; sin embargo, el patrón en México es demasiado impreciso, pues sólo requiere identificar si éste es menor o mayor a 30 meses. Esta fal
Externí odkaz:
https://doaj.org/article/08df1c2b345e4b95bf8afa75d835eacb
Publikováno v:
COLING
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 co
Autor:
Ajay Nagesh, Mihai Surdeanu, Andrew Zupon, Marco Antonio Valenzuela-Escárcega, Maria Alexeeva
Publikováno v:
SPNLP@NAACL-HLT
We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i.e., use of limited annotations and interpretability of extraction patterns, wi
Autor:
Mihai Surdeanu, Marco Antonio Valenzuela-Escárcega, George Caique Gouveia Barbosa, Zechy Wong, Rebecca Sharp, Gus Hahn-Powell, Dane Bell
Publikováno v:
NAACL-HLT (Demonstrations)
Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions
Autor:
Ajay Nagesh, Zheng Tang, Vikas Yadav, Steven Bethard, Keith Alcock, Egoitz Laparra, Marco Antonio Valenzuela-Escárcega, Benjamin M. Gyori, Fan Luo, Mihai Surdeanu, Clayton T. Morrison, Adarsh Pyarelal, John A. Bachman, Rebecca Sharp, Heather C. Lent, Mithun Paul, Kobus Barnard
Publikováno v:
NAACL-HLT (Demonstrations)
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed appr
Autor:
Thomas Hicks, Enrique Noriega-Atala, Emek Demir, Marco Antonio Valenzuela-Escárcega, Özgün Babur, Clayton T. Morrison, Gus Hahn-Powell, Mihai Surdeanu, Dane Bell, Xia Wang
Publikováno v:
Database: The Journal of Biological Databases and Curation
PubMed, a repository and search engine for biomedical literature, now indexes >1 million articles each year. This exceeds the processing capacity of human domain experts, limiting our ability to truly understand many diseases. We present Reach, a sys
Publikováno v:
TextGraphs@NAACL-HLT
We introduce a machine learning approach for the identification of “white spaces” in scientific knowledge. Our approach addresses this task as link prediction over a graph that contains over 2M influence statements such as “CTCF activates FOXA1
Autor:
Marco Antonio Valenzuela-Escárcega, Mihai Surdeanu, Enrique Noriega-Atala, Clayton T. Morrison
Publikováno v:
EMNLP
Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b37f9486bd3036809e968d1cd7897d3
http://arxiv.org/abs/1709.00149
http://arxiv.org/abs/1709.00149
Publikováno v:
ACL (System Demonstrations)
Publikováno v:
BioNLP@ACL
We propose an approach for biomedical information extraction that marries the advantages of machine learning models, e.g., learning directly from data, with the benefits of rule-based approaches, e.g., interpretability. Our approach starts by trainin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7cd0e350b19f9326f23f667c082a272