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