165 research outputs found
Establishing a New State-of-the-Art for French Named Entity Recognition
The French TreeBank developed at the University Paris 7 is the main source of
morphosyntactic and syntactic annotations for French. However, it does not
include explicit information related to named entities, which are among the
most useful information for several natural language processing tasks and
applications. Moreover, no large-scale French corpus with named entity
annotations contain referential information, which complement the type and the
span of each mention with an indication of the entity it refers to. We have
manually annotated the French TreeBank with such information, after an
automatic pre-annotation step. We sketch the underlying annotation guidelines
and we provide a few figures about the resulting annotations
Discourse-sensitive automatic identification of generic expressions
This paper describes a novel sequence labeling method for identifying generic expressions, which refer to kinds or arbitrary members of a class, in discourse context. The automatic recognition of such expressions is important for any natural language processing task that requires text understanding. Prior work has focused on identifying generic noun phrases; we present a new corpus in which not only subjects but also clauses are annotated for genericity according to an annotation scheme motivated by semantic theory. Our contextaware approach for automatically identifying generic expressions uses conditional random fields and outperforms previous work based on local decisions when evaluated on this corpus and on related data sets (ACE-2 and ACE-2005)
A dual ascent framework for Lagrangean decomposition of combinatorial problems
We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems. Although methods of this type have shown their efficiency for a number of problems, so far there was no general algorithm applicable to multiple problem types. In this work, we propose such a general algorithm. It depends on several parameters, which can be used to optimize its performance in each particular setting. We demonstrate efficacy of our method on graph matching and multicut problems, where it outperforms state-of-the-art solvers including those based on subgradient optimization and off-the-shelf linear programming solvers
A dual ascent framework for Lagrangean decomposition of combinatorial problems
We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems. Although methods of this type have shown their efficiency for a number of problems, so far there was no general algorithm applicable to multiple problem types. In this work, we propose such a general algorithm. It depends on several parameters, which can be used to optimize its performance in each particular setting. We demonstrate efficacy of our method on graph matching and multicut problems, where it outperforms state-of-the-art solvers including those based on subgradient optimization and off-the-shelf linear programming solvers
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