Data Mining Practical Machine Learning Tools and Techniques

我的台灣, 看見心靈的故鄉 =2009林磐聳藝術與設計展

he Arabic language has recently become the focus of an increasing number of projects in natural language processing (NLP) and computational linguistics (CL). In this book, I try to provide NLP/CL system developers and researchers (computer scientists and linguists alike) with the necessary background information for working with Arabic.I discuss various Arabic linguistic phenomena and review the state-of-the-art in Arabic processing.

Introduction to Arabic Natural Language Processing

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영어의 능격성(Ergativity)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.

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Findings of the 2019 Conference on Machine Translation (WMT19)

This paper describes the simultaneous development of dependency structure and phrase structure treebanks for Hindi and Urdu, as well as a PropBank. The dependency structure and the PropBank are manually annotated, and then the phrase structure treebank is produced automatically. To ensure successful conversion the development of the guidelines for all three representations are carefully coordinated.

A Multi-Representational and Multi-Layered Treebank for Hindi/Urdu

The paper introduces a dependency annotation effort which aims to fully annotate a million word Hindi corpus. It is the first attempt of its kind to develop a large scale tree-bank for an Indian language. In this paper we provide the motivation for following the Paninian framework as the annotation scheme and argue that the Paninian framework is better suited to model the various linguistic phenomena manifest in Indian languages. We present the basic annotation scheme. We also show how the scheme handles some phenomenon such as complex verbs, ellipses, etc. Empirical results of some experiments done on the currently annotated sentences are also reported.

Dependency Annotation Scheme for Indian Languages

This paper describes a treebanking project for Hindi/Urdu. We are annotating dependency syntax, lexical predicate-argument structure, and phrase structure syntax in a coordinated and partly automated manner. The paper focuses on choices in syntactic representation, and the stages we think are most appropriate for annotating differnt types of information.

Hindi Syntax: Annotating Dependency, Lexical Predicate-Argument Structure, and Phrase Structure

We describe the Hindi Discourse Relation Bank project, aimed at developing a large corpus annotated with discourse relations. We adopt the lexically grounded approach of the Penn Discourse Treebank, and describe our classification of Hindi discourse connectives, our modifications to the sense classification of discourse relations, and some cross-linguistic comparisons based on some initial annotations carried out so far.

The Hindi Discourse Relation Bank

In this study, the problem of shallow parsing of Hindi-English code-mixed social media text (CSMT) has been addressed. We have annotated the data, developed a language identifier, a normalizer, a part-of-speech tagger and a shallow parser. To the best of our knowledge, we are the first to attempt shallow parsing on CSMT. The pipeline developed has been made available to the research community with the goal of enabling better text analysis of Hindi English CSMT. The pipeline is accessible at 1 .

Dipti Misra Sharma

Papers

A Multi-Representational and Multi-Layered Treebank for Hindi/Urdu

Dependency Annotation Scheme for Indian Languages

Hindi Syntax: Annotating Dependency, Lexical Predicate-Argument Structure, and Phrase Structure

The Hindi Discourse Relation Bank

Shallow Parsing Pipeline for Hindi-English Code-Mixed Social Media Text