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JMLR
2010
134views more  JMLR 2010»
12 years 11 months ago
Using Contextual Representations to Efficiently Learn Context-Free Languages
We present a polynomial update time algorithm for the inductive inference of a large class of context-free languages using the paradigm of positive data and a membership oracle. W...
Alexander Clark, Rémi Eyraud, Amaury Habrar...
ALT
2003
Springer
13 years 8 months ago
Can Learning in the Limit Be Done Efficiently?
Abstract. Inductive inference can be considered as one of the fundamental paradigms of algorithmic learning theory. We survey results recently obtained and show their impact to pot...
Thomas Zeugmann
EMNLP
2010
13 years 2 months ago
Collective Cross-Document Relation Extraction Without Labelled Data
We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text. In particular, we tackle ...
Limin Yao, Sebastian Riedel, Andrew McCallum
JMLR
2012
11 years 7 months ago
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its s...
Antoine Bordes, Xavier Glorot, Jason Weston, Yoshu...
EMNLP
2010
13 years 3 months ago
Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models
We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to partof-speech (POS) tagging. The algorithm uses a simil...
Amarnag Subramanya, Slav Petrov, Fernando Pereira