1,357 research outputs found
Lending for learning : twenty years of World Bank support for basic education
The author traces the development of the World Bank's lending policies for education and draws lessons and recommendations from the Bank's experience. The Bank's lending for primary education has supported four main objectives : expanding educational opportunities, improving instructional quality, increasing efficiency, and strengthening management in the sector. In nonformal education, Bank lending has supported the goals of developing practical skills, promoting basic literacy, and building income generating skills. The author argues that Bank support to education has been most successful when it provides for in-depth analysis of subsectoral issues, concentrates on a few objectives, sustains its committment to these objectives over a long period, and delegates to the borrowing country the responsibility for sectoral analysis, policy formulation, and project development and implementation. From his review of Bank experience in supporting basic education, the author makes five principal recommendations for designing education projects : 1) support the locally determined processes that drive educational development; 2) invest in the most cost-effective inputs; 3) test carefully how an investment package works in a particular setting and monitor outcomes constantly; 4) strengthen the institutional capacity for national and regional strategic planning and management; and 5) design projects to allow a flexible response to a wide variety of local needs and unplanned events.Teaching and Learning,Curriculum&Instruction,Primary Education,Gender and Education,Girls Education
An improved neural network model for joint POS tagging and dependency parsing
We propose a novel neural network model for joint part-of-speech (POS)
tagging and dependency parsing. Our model extends the well-known BIST
graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating
a BiLSTM-based tagging component to produce automatically predicted POS tags
for the parser. On the benchmark English Penn treebank, our model obtains
strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+%
absolute improvements to the BIST graph-based parser, and also obtaining a
state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental
results on parsing 61 "big" Universal Dependencies treebanks from raw texts
show that our model outperforms the baseline UDPipe (Straka and Strakov\'a,
2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS
score. In addition, with our model, we also obtain state-of-the-art downstream
task scores for biomedical event extraction and opinion analysis applications.
Our code is available together with all pre-trained models at:
https://github.com/datquocnguyen/jPTDPComment: 11 pages; In Proceedings of the CoNLL 2018 Shared Task: Multilingual
Parsing from Raw Text to Universal Dependencies, to appea
A Framework to Adjust Dependency Measure Estimates for Chance
Estimating the strength of dependency between two variables is fundamental
for exploratory analysis and many other applications in data mining. For
example: non-linear dependencies between two continuous variables can be
explored with the Maximal Information Coefficient (MIC); and categorical
variables that are dependent to the target class are selected using Gini gain
in random forests. Nonetheless, because dependency measures are estimated on
finite samples, the interpretability of their quantification and the accuracy
when ranking dependencies become challenging. Dependency estimates are not
equal to 0 when variables are independent, cannot be compared if computed on
different sample size, and they are inflated by chance on variables with more
categories. In this paper, we propose a framework to adjust dependency measure
estimates on finite samples. Our adjustments, which are simple and applicable
to any dependency measure, are helpful in improving interpretability when
quantifying dependency and in improving accuracy on the task of ranking
dependencies. In particular, we demonstrate that our approach enhances the
interpretability of MIC when used as a proxy for the amount of noise between
variables, and to gain accuracy when ranking variables during the splitting
procedure in random forests.Comment: In Proceedings of the 2016 SIAM International Conference on Data
Minin
Towards a Semantic Lexicon for Biological Language Processing
This paper explores the use of the resources in the National Library of Medicine's
Unified Medical Language System (UMLS) for the construction of a lexicon useful
for processing texts in the field of molecular biology. A lexicon is constructed from
overlapping terms in the UMLS SPECIALIST lexicon and the UMLS Metathesaurus
to obtain both morphosyntactic and semantic information for terms, and the coverage
of a domain corpus is assessed. Over 77% of tokens in the domain corpus are found in
the constructed lexicon, validating the lexicon's coverage of the most frequent terms
in the domain and indicating that the constructed lexicon is potentially an important
resource for biological text processing
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