Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Jesse Dunietz"'
Autor:
Lisa Volpatti, Alex Hanson, Jennifer Schall, Jesse Dunietz, Amanda Chen, Rohan Chitnis, Eric Alm, Alison Takemura, Diana Chien
Publikováno v:
2020 ASEE Virtual Annual Conference Content Access Proceedings.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb903ee79de23975633d77ad2d2ac9ea
https://doi.org/10.18260/1-2--35117
https://doi.org/10.18260/1-2--35117
Autor:
Jesse Dunietz, David A. Ferrucci, Jennifer Chu-Carroll, Akash Bharadwaj, Owen Rambow, Gregory Burnham
Publikováno v:
ACL
Many tasks aim to measure machine reading comprehension (MRC), often focusing on question types presumed to be difficult. Rarely, however, do task designers start by considering what systems should in fact comprehend. In this paper we make two key co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c40213f7d113abefe0b77a2c9880b9d
http://arxiv.org/abs/2005.01525
http://arxiv.org/abs/2005.01525
Publikováno v:
Transactions of the Association for Computational Linguistics. 5:117-133
This paper explores extending shallow semantic parsing beyond lexical-unit triggers, using causal relations as a test case. Semantic parsing becomes difficult in the face of the wide variety of linguistic realizations that causation can take on. We t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6080221582a6e74eaa2e9acf7a3a0f3
https://doi.org/10.1162/tacl_a_00050
https://doi.org/10.1162/tacl_a_00050
Autor:
Jesse, Dunietz
Publikováno v:
Scientific American. 317:14-16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5eda3f1b75764cae5d2c7c88465ce201
https://doi.org/10.1038/scientificamerican1017-14
https://doi.org/10.1038/scientificamerican1017-14
Publikováno v:
EMNLP
This paper introduces the surface construction labeling (SCL) task, which expands the coverage of Shallow Semantic Parsing (SSP) to include frames triggered by complex constructions. We present DeepCx, a neural, transition-based system for SCL. As a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e36e500a73682046b47a540d9eedcef0
https://doi.org/10.18653/v1/d18-1196
https://doi.org/10.18653/v1/d18-1196
Publikováno v:
LAW@ACL
Language of cause and effect captures an essential component of the semantics of a text. However, causal language is also intertwined with other semantic relations, such as temporal precedence and correlation. This makes it difficult to determine whe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12850760eeee3282c887ed040cd1d4e2
https://doi.org/10.18653/v1/w17-0812
https://doi.org/10.18653/v1/w17-0812
Publikováno v:
LAW@NAACL-HLT
Detecting and analyzing causal language is essential to extracting semantic relationships. To that end, we present an annotation scheme for English causal language (not metaphysical causality), and discuss two methodologies for annotation. The first
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3888450faced9d5e7457ebaf84823bc0
https://doi.org/10.3115/v1/w15-1622
https://doi.org/10.3115/v1/w15-1622
Autor:
Jesse Dunietz, Daniel Gillick
Publikováno v:
EACL
Although many NLP systems are moving toward entity-based processing, most still identify important phrases using classical keyword-based approaches. To bridge this gap, we introduce the task of entity salience: assigning a relevance score to each ent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a0c4c2bc8109aa8f0c36490ffa3d99a
https://doi.org/10.3115/v1/e14-4040
https://doi.org/10.3115/v1/e14-4040
Autor:
Jesse Dunietz
To ease the tasks of Monte Carlo (MC) simulation and event reconstruction (i.e. inferring particle-decay events from experimental data) for long-term BaBar data preservation and analysis, the following software components have been designed: a langua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6dcc3530039e71416bd5f5a8bd919e85
https://doi.org/10.2172/1017224
https://doi.org/10.2172/1017224