226 research outputs found
A study on iterative methods for solving Richards` equation
This work concerns linearization methods for efficiently solving the
Richards` equation,a degenerate elliptic-parabolic equation which models flow
in saturated/unsaturated porous media.The discretization of Richards` equation
is based on backward Euler in time and Galerkin finite el-ements in space. The
most valuable linearization schemes for Richards` equation, i.e. the
Newtonmethod, the Picard method, the Picard/Newton method and theLscheme are
presented and theirperformance is comparatively studied. The convergence, the
computational time and the conditionnumbers for the underlying linear systems
are recorded. The convergence of theLscheme is theo-retically proved and the
convergence of the other methods is discussed. A new scheme is
proposed,theLscheme/Newton method which is more robust and quadratically
convergent. The linearizationmethods are tested on illustrative numerical
examples
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
The state-of-the-art named entity recognition (NER) systems are supervised
machine learning models that require large amounts of manually annotated data
to achieve high accuracy. However, annotating NER data by human is expensive
and time-consuming, and can be quite difficult for a new language. In this
paper, we present two weakly supervised approaches for cross-lingual NER with
no human annotation in a target language. The first approach is to create
automatically labeled NER data for a target language via annotation projection
on comparable corpora, where we develop a heuristic scheme that effectively
selects good-quality projection-labeled data from noisy data. The second
approach is to project distributed representations of words (word embeddings)
from a target language to a source language, so that the source-language NER
system can be applied to the target language without re-training. We also
design two co-decoding schemes that effectively combine the outputs of the two
projection-based approaches. We evaluate the performance of the proposed
approaches on both in-house and open NER data for several target languages. The
results show that the combined systems outperform three other weakly supervised
approaches on the CoNLL data.Comment: 11 pages, The 55th Annual Meeting of the Association for
Computational Linguistics (ACL), 201
Bilateral Multi-Perspective Matching for Natural Language Sentences
Natural language sentence matching is a fundamental technology for a variety
of tasks. Previous approaches either match sentences from a single direction or
only apply single granular (word-by-word or sentence-by-sentence) matching. In
this work, we propose a bilateral multi-perspective matching (BiMPM) model
under the "matching-aggregation" framework. Given two sentences and ,
our model first encodes them with a BiLSTM encoder. Next, we match the two
encoded sentences in two directions and . In
each matching direction, each time step of one sentence is matched against all
time-steps of the other sentence from multiple perspectives. Then, another
BiLSTM layer is utilized to aggregate the matching results into a fix-length
matching vector. Finally, based on the matching vector, the decision is made
through a fully connected layer. We evaluate our model on three tasks:
paraphrase identification, natural language inference and answer sentence
selection. Experimental results on standard benchmark datasets show that our
model achieves the state-of-the-art performance on all tasks.Comment: To appear in Proceedings of IJCAI 201
THE ISSUE OF LIABILITY FOR NON-PATRIMONIAL PREJUDICE WITHIN THE LEGAL LABOR RELATIONS
This study aims to examine the extent to which legal liability for non-materialprejudices and moral prejudices is allowable in the field of legal labour relations, in thecontext of contest for regulations governing non-patrimonial prejudices in Romanianlegislation
THE STATUS OF EMPLOYED OF AN OFFENDER β CONDITION TO ATTRACT PATRIMONIAL LIABILITY
Financial liability of the employee is a variety of contractual liability arising from specific features of legal work. This study aims to analyze the concrete situations in which the patrimony can be held liable to the employee, given the diversity of employment situations in which an employee may be engaged
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