Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Kuzman Ganchev"'
Publikováno v:
Transactions of the Association for Computational Linguistics. 3:29-41
We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either appr
Publikováno v:
EMNLP
A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe5ec7c1f7d558d6826a9fcd5268ccdc
http://arxiv.org/abs/1808.06511
http://arxiv.org/abs/1808.06511
Autor:
Michael Collins, Aliaksei Severyn, David J. Weiss, Daniel Andor, Alessandro Presta, Slav Petrov, Kuzman Ganchev, Chris Alberti
Publikováno v:
ACL (1)
We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that operates on a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c92b226c29e208f0340c1ae891d8b55a
http://arxiv.org/abs/1603.06042
http://arxiv.org/abs/1603.06042
Publikováno v:
ResearcherID
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text. The standard maximum-likelihood hidden Markov model for this task performs poorly, because of its weak inductive bias and large mod
Publikováno v:
Computational Linguistics. 36:481-504
Word-level alignment of bilingual text is a critical resource for a growing variety of tasks. Probabilistic models for word alignment present a fundamental trade-off between richness of captured constraints and correlations versus efficiency and trac
Publikováno v:
EMNLP
We present a new method for semantic role labeling in which arguments and semantic roles are jointly embedded in a shared vector space for a given predicate. These embeddings belong to a neural network, whose output represents the potential functions
We introduce and analyze a natural algorithm for multi-venue exploration from censored data, which is motivated by the Dark Pool Problem of modern quantitative finance. We prove that our algorithm converges in polynomial time to a near-optimal alloca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c689ac0743cb45c9ce4597bc7cd0a47b
http://arxiv.org/abs/1205.2646
http://arxiv.org/abs/1205.2646
Publikováno v:
ACL/IJCNLP
Broad-coverage annotated treebanks necessary to train parsers do not exist for many resource-poor languages. The wide availability of parallel text and accurate parsers in English has opened up the possibility of grammar induction through partial tra
Publikováno v:
The Prague Bulletin of Mathematical Linguistics. 91
In this paper we present a new open-source toolkit for statistical word alignments - Posterior Constrained Alignment Toolkit (PostCAT). e toolkit implements three well known word alignment algorithms (IBM M1, IBM M2, HMM) as well as six new models
Publikováno v:
BioNLP@HLT-NAACL (Shared Task)
We describe the system of the PIKB team for BioNLP'09 Shared Task 1, which targets tunable domain-independent event extraction. Our approach is based on a three-stage classification: (1) trigger word tagging, (2) simple event extraction, and (3) comp