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Words in Time and Place: Exploring Language Through the Historical Thesaurus of the Oxford English Dictionary

By David Crystal

Offers a unique view of the English language and its development, and includes witty commentary and anecdotes along the way.


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The Indo-European Controversy: Facts and Fallacies in Historical Linguistics

By Asya Pereltsvaig and Martin W. Lewis

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Academic Paper


Title: Machine learning-based named entity recognition via effective integration of various evidences
Author: Guodong Zhou
Institution: Institute for Infocomm Research
Author: Jian Su
Institution: Institute for Infocomm Research
Linguistic Field: Applied Linguistics
Abstract: Named entity recognition identifies and classifies entity names in a text document into some predefined categories. It resolves the 'who', 'where' and 'how much' problems in information extraction and leads to the resolution of the 'what' and 'how' problems in further processing. This paper presents a Hidden Markov Model (HMM) and proposes a HMM-based named entity recognizer implemented as the system PowerNE. Through the HMM and an effective constraint relaxation algorithm to deal with the data sparseness problem, PowerNE is able to effectively apply and integrate various internal and external evidences of entity names. Currently, four evidences are included: (1) a simple deterministic internal feature of the words, such as capitalization and digitalization; (2) an internal semantic feature of the important triggers; (3) an internal gazetteer feature, which determines the appearance of the current word string in the provided gazetteer list; and (4) an external macro context feature, which deals with the name alias phenomena. In this way, the named entity recognition problem is resolved effectively. PowerNE has been benchmarked with the Message Understanding Conferences (MUC) data. The evaluation shows that, using the formal training and test data of the MUC-6 and MUC-7 English named entity tasks, and it achieves the F-measures of 96.6 and 94.1, respectively. Compared with the best reported machine learning system, it achieves a 1.7 higher F-measure with one quarter of the training data on MUC-6, and a 3.6 higher F-measure with one ninth of the training data on MUC-7. In addition, it performs slightly better than the best reported handcrafted rule-based systems on MUC-6 and MUC-7.

CUP AT LINGUIST

This article appears IN Natural Language Engineering Vol. 11, Issue 2, which you can READ on Cambridge's site or on LINGUIST .



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