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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  • 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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  • 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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  • This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization. We first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. ...

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  • Computational linguistics is generally considered to be the branch of engineering that uses computers to do useful things with linguistic signals, but it can also be viewed as an extended test of computational theories of human cognition; it is this latter perspective that psychologists find most interesting. Language provides a critical test for the hypothesis that physical symbol systems are adequate to perform all human cognitive functions.

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  • We demonstrate a system for flexible querying against text that has been annotated with the results of NLP processing. The system supports self-overlapping and parallel layers, integration of syntactic and ontological hierarchies, flexibility in the format of returned results, and tight integration with SQL. We present a query language and its use on examples taken from the NLP literature.

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  • We have developed an approach to natural language processing in which the natural language processor is viewed as a knowledge-based system whose knowledge is about the meanings of the utterances of its language. The approach is orzented around the phrase rather than the word as the basic unit. We believe that this p a r a d i ~ for language processing not only extends the capabilities of other natural language systems, but handles those tasks that previous systems could perform in e more systematic and extensible manner. ...

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  • This paper proposes a method for organizing linguistic knowledge in both systematic and flexible fashion. We introduce a purely applicative language (PAL) as an intermediate representation and an object-oriented computation mechanism for its interpretation. PAL enables the establishment of a principled and well-constrained method of interaction among lexicon-oriented linguistic modules. The object-oriented computation mechanism provides a flexible means of abstracting modules and sharing common knowledge. ...

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  • In the field of knowledge based systems for natural language processing, one of the most challenging aims is to use parts of an existing knowledge base for different domains and/or different tasks. We support the point that this problem can only be solved by using adequate metainformation about the content and structuring principles of the representational systems concerned. One of the prerequisites in this respect is the transparency of modelling decisions.

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  • This paper presents an efficient inference algorithm of conditional random fields (CRFs) for large-scale data. Our key idea is to decompose the output label state into an active set and an inactive set in which most unsupported transitions become a constant. Our method unifies two previous methods for efficient inference of CRFs, and also derives a simple but robust special case that performs faster than exact inference when the active sets are sufficiently small. We demonstrate that our method achieves dramatic speedup on six standard natural language processing problems. ...

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