163,776 research outputs found

    Query processing in Chiql: optimization and translation.

    Get PDF
    by Yip Suen-man.Appendixes in Chinese and English.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references.Acknowledgment --- p.1Abstract --- p.2Table of Contents --- p.3List of Tables --- p.5List of Figures --- p.6Chapter Chapter 1 --- Introduction --- p.7Chapter 1.1 --- Objectives --- p.9Chapter 1.2 --- Chapter Summary --- p.10Chapter Chapter 2 --- Related Work --- p.11Chapter 2.1 --- Relational Query Language --- p.11Chapter 2.1.1 --- Relational Algebra Vs Relational Calculus --- p.11Chapter 2.1.2 --- Procedural Vs Nonprocedural --- p.13Chapter 2.1.3 --- Natural Language (NL) Vs Restricted Natural Language (RNL) --- p.13Chapter 2.2 --- Existing Relational Query Language --- p.14Chapter 2.3 --- Chinese Related Work --- p.16Chapter 2.4 --- Chapter Summary --- p.17Chapter Chapter 3 --- Chinese Database Query Language : Chiql --- p.19Chapter 3.1 --- Naturalness --- p.19Chapter 3.2 --- Simplicity --- p.20Chapter 3.3 --- Procedural and Multi-statements Query Style --- p.21Chapter 3.4 --- Functional Completeness --- p.22Chapter 3.5 --- Chapter Summary --- p.25Chapter Chapter 4 --- Query Processing --- p.26Chapter 4.1 --- Query Optimization --- p.27Chapter 4.1.1 --- Query Representation --- p.27Chapter 4.1.2 --- Standardization --- p.28Chapter 4.1.3 --- Simplification --- p.29Chapter 4.1.4 --- Amelioration --- p.29Chapter 4.2 --- Query Translation of SQL --- p.29Chapter 4.3 --- Query Processing in Chiql --- p.33Chapter 4.3.1 --- Overview of the Query Processing --- p.33Chapter 4.3.2 --- Inter-Statement Dependency --- p.34Chapter 4.3.3 --- Translation flow of Chiql-to-SQL --- p.36Chapter 4.3.4 --- An Introductory Example --- p.37Chapter 4.4 --- Chapter Summary --- p.40Chapter Chapter 5 --- Statement Merging Algorithm (SMA) --- p.41Chapter 5.1 --- Problems --- p.41Chapter 5.2 --- Definitions --- p.42Chapter 5.3 --- Linear Merging Algorithm (LMA) --- p.43Chapter 5.4 --- Tree Merging Algorithm (TMA) --- p.47Chapter 5.5 --- Statement Merging Algorithm (SMA) --- p.50Chapter 5.6 --- Improvement --- p.56Chapter 5.7 --- Chapter Summary --- p.57Chapter Chapter 6 --- Pattern Mapping Algorithm (PMA) --- p.58Chapter 6.1 --- Problem --- p.58Chapter 6.2 --- Type of Patterns --- p.61Chapter 6.3 --- Pre-requisite of Pattern Mapping --- p.65Chapter 6.4 --- Pattern Mapping Algorithm (PMA) --- p.65Chapter 6.5 --- An Illustration Example --- p.68Chapter 6.6 --- Chapter Summary --- p.72Chapter Chapter 7 --- Evaluation --- p.73Chapter 7.1 --- Testing the Correctness --- p.73Chapter 7.2 --- Comparison in Translation Power With Other Translator --- p.76Chapter 7.3 --- Chapter Summary --- p.78Chapter Chapter 8 --- Conclusion --- p.79Reference --- p.82Appendix --- p.8

    Chinese–Spanish neural machine translation enhanced with character and word bitmap fonts

    Get PDF
    Recently, machine translation systems based on neural networks have reached state-of-the-art results for some pairs of languages (e.g., German–English). In this paper, we are investigating the performance of neural machine translation in Chinese–Spanish, which is a challenging language pair. Given that the meaning of a Chinese word can be related to its graphical representation, this work aims to enhance neural machine translation by using as input a combination of: words or characters and their corresponding bitmap fonts. The fact of performing the interpretation of every word or character as a bitmap font generates more informed vectorial representations. Best results are obtained when using words plus their bitmap fonts obtaining an improvement (over a competitive neural MT baseline system) of almost six BLEU, five METEOR points and ranked coherently better in the human evaluation.Peer ReviewedPostprint (published version

    Implementation of Rule Based Algorithm for Sandhi-Vicheda Of Compound Hindi Words

    Get PDF
    Sandhi means to join two or more words to coin new word. Sandhi literally means `putting together' or combining (of sounds), It denotes all combinatory sound-changes effected (spontaneously) for ease of pronunciation. Sandhi-vicheda describes [5] the process by which one letter (whether single or cojoined) is broken to form two words. Part of the broken letter remains as the last letter of the first word and part of the letter forms the first letter of the next letter. Sandhi-Vicheda is an easy and interesting way that can give entirely new dimension that add new way to traditional approach to Hindi Teaching. In this paper using the Rule based algorithm we have reported an accuracy of 60-80% depending upon the number of rules to be implemented

    Language Without Words: A Pointillist Model for Natural Language Processing

    Full text link
    This paper explores two separate questions: Can we perform natural language processing tasks without a lexicon?; and, Should we? Existing natural language processing techniques are either based on words as units or use units such as grams only for basic classification tasks. How close can a machine come to reasoning about the meanings of words and phrases in a corpus without using any lexicon, based only on grams? Our own motivation for posing this question is based on our efforts to find popular trends in words and phrases from online Chinese social media. This form of written Chinese uses so many neologisms, creative character placements, and combinations of writing systems that it has been dubbed the "Martian Language." Readers must often use visual queues, audible queues from reading out loud, and their knowledge and understanding of current events to understand a post. For analysis of popular trends, the specific problem is that it is difficult to build a lexicon when the invention of new ways to refer to a word or concept is easy and common. For natural language processing in general, we argue in this paper that new uses of language in social media will challenge machines' abilities to operate with words as the basic unit of understanding, not only in Chinese but potentially in other languages.Comment: 5 pages, 2 figure
    • …
    corecore