Natural language processing

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  • BOOK DESCRIPTION This book offers a highly accessible introduction to Natural Language Processing, the field that underpins a variety of language technologies, ranging from predictive text and email filtering to automatic summarization and translation. With Natural Language Processing with Python, you’ll learn how to write Python programs to work with large collections of unstructured text. You’ll access richly-annotated datasets using a comprehensive range of linguistic data structures.

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  • This demonstration presents the Annotation Librarian, an application programming interface that supports rapid development of natural language processing (NLP) projects built in Apache Unstructured Information Management Architecture (UIMA). The flexibility of UIMA to support all types of unstructured data – images, audio, and text – increases the complexity of some of the most common NLP development tasks.

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  • Bài giảng Xử lý ngôn ngữ tự nhiên (Natural Language Processing): Bài 1 cung cấp cho người học những kiến thức cơ bản như: Hiểu các nguyên tắc cơ bản và các cách tiếp cận trong xử lý ngôn ngữ tự nhiên, học được các công cụ và kỹ thuật có thể dùng để phát triển các hệ thống hiểu văn bản và nói chuyện với con người. Mời các bạn cùng tham khảo.

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  • We present Wikulu1 , a system focusing on supporting wiki users with their everyday tasks by means of an intelligent interface. Wikulu is implemented as an extensible architecture which transparently integrates natural language processing (NLP) techniques with wikis. It is designed to be deployed with any wiki platform, and the current prototype integrates a wide range of NLP algorithms such as keyphrase extraction, link discovery, text segmentation, summarization, or text similarity.

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  • We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effectiveness of traditional key-word based document retrieval. The backbone of our system is a statistical retrieval engine which performs automated indexing of documents, then search and ranking in response to user queries. This core architecture is augmented with advanced natural language processing tools which are both robust and efficient.

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  • This is the story of how one of the new natural language processing products reached the marketplace. On the surface, it is the story of one NL researcherturned-entrepreneur (yours truly) and of one product, Q&A. But this is not just my story: It is in microcosm the story of NL emerging from the confines of the academic world, which in turn is an instance of the old theme "science goes commercial."

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  • In this paper* I will argue for a model of grammatical processing that is based on uniform processing and knowledge sources. The main feature of this model is to view parsing and generation as two strongly interleaved tasks performed by a single parametrized deduction process. It will be shown that this view supports flexible and efficient natural language processing.

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  • In recent years, machine learning (ML) has been used more and more to solve complex tasks in different disciplines, ranging from Data Mining to Information Retrieval or Natural Language Processing (NLP). These tasks often require the processing of structured input, e.g., the ability to extract salient features from syntactic/semantic structures is critical to many NLP systems. Mapping such structured data into explicit feature vectors for ML algorithms requires large expertise, intuition and deep knowledge about the target linguistic phenomena.

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  • Introduction: Most research in machine learning has been focused on binary classification, in which the learned classifier outputs one of two possible answers. Important fundamental questions can be analyzed in terms of binary classification, but realworld natural language processing problems often involve richer output spaces. In this tutorial, we will focus on classifiers with a large number of possible outputs with interesting structure. Notable examples include information retrieval, part-of-speech tagging, NP chucking, parsing, entity extraction, and phoneme recognition. ...

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  • Convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing (NLP) tasks. Experiments have, however, shown that the over-fitting problem often arises when these kernels are used in NLP tasks. This paper discusses this issue of convolution kernels, and then proposes a new approach based on statistical feature selection that avoids this issue. To enable the proposed method to be executed efficiently, it is embedded into an original kernel calculation process by using sub-structure mining algorithms. ...

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  • Lecture “Natural language processing - Chapter 1: Introduction and Overview of NLP” has contents: Introduce some of the classical problems in NLP, learn to address empirical problems, talk/write clearly about your work, decision and observations.

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  • Lecture “Natural language processing - Chapter 2: Fundamental algorithms and mathematical models” has contents: Probability theory and Bayes theorems (Concepts in probability, Bayes theorems, application of the probability theory in NLP).

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  • Lecture “Natural language processing – Chapter 3: Basic principles for NLP” has contents: POS – part of speech tagging, POS – part of speech examples for English, POS – Methods of tagging, sentence types,…and other contents.

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  • Lecture “Natural language processing – Chapter 4: Computational linguistics” has contents: What is computational linguistics, corpus definitions, corpus categories, parallel corpora application, alignment methods, normalization, lemmatization and tokenization.

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  • Lecture “Natural language processing – Chapter 5: Foundation of statistical machine translation” has contents: Introduction to statistical machine translation, statistical MT systems, three problems in statistical MT systems, translation model, and other contents.

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  • An algorithm based on the Generalized Hebbian Algorithm is described that allows the singular value decomposition of a dataset to be learned based on single observation pairs presented serially. The algorithm has minimal memory requirements, and is therefore interesting in the natural language domain, where very large datasets are often used, and datasets quickly become intractable. The technique is demonstrated on the task of learning word and letter bigram pairs from text.

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  • This paper describes a natural language pro- be improved by enhancing underlying compocessing system reinforced by the use of associ- nent technologies, such as knowledge based ation of words and concepts, implemented as a systems. In particular, alternate approaches neural network. Combining an associative net- to symbolic manipulation provided by connecwork with a conventional system contributes tionist models [Rumelhart 86] have emerged. to semantic disambiguation in the process of Connectionist approaches enable the extracinterpretation. ...

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  • it is widely reco~nlzed that the process of understandln~ natural language texts cannot be accomplished w i t h o u t accessin~ mundane Knowledge a b o u t the w o r l d [2, 4, 6, 7]. That is, in order to resolve ambiguities, form expectations, and make causal connections between events, we must make use of all sorts of episodic, stereotypic and factual knowledge. In this p a p e r , we are concerned with the way functional knowledge of objects, and associations between objects can be exploited in an understandln~ system. ...

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  • Logic programming, an important new method of compute programming resulting from recent research in artifucial intelligence and computer science, has proved to be especially appropriate for solving problems in natrual-language processing. "Prolog and Natural Language Analysis" provides a concise and practical introduction to logic programming and the logic-programming language Prolog both as vehicles for understanding elementary computational linguistics and as tools for implementing the basic components of natural-language-processing systems....

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  • The Natural Language Toolkit is a suite of program modules, data sets and tutorials supporting research and teaching in computational linguistics and natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past year the toolkit has been rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python language. This paper reports on the simplified toolkit and explains how it is used in teaching NLP....

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