This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress.

A survey of named entity recognition and classification

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

A grammar of spoken Chinese = 中國話的文法

Ethnologue: Languages of the World

Each year the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their systems on exactly the same data sets, in order to better compare systems. The tenth CoNLL (CoNLL-X) saw a shared task on Multilingual Dependency Parsing. In this paper, we describe how treebanks for 13 languages were converted into the same dependency format and how parsing performance was measured. We also give an overview of the parsing approaches that participants took and the results that they achieved. Finally, we try to draw general conclusions about multi-lingual parsing: What makes a particular language, treebank or annotation scheme easier or harder to parse and which phenomena are challenging for any dependency parser?

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CoNLL-X Shared Task on Multilingual Dependency Parsing

Named Entity (NE) extraction is an important subtask of document processing such as information extraction and question answering. A typical method used for NE extraction of Japanese texts is a cascade of morphological analysis, POS tagging and chunking. However, there are some cases where segmentation granularity contradicts the results of morphological analysis and the building units of NEs, so that extraction of some NEs are inherently impossible in this setting. To cope with the unit problem, we propose a character-based chunking method. Firstly, the input sentence is analyzed redundantly by a statistical morphological analyzer to produce multiple (n-best) answers. Then, each character is annotated with its character types and its possible POS tags of the top n-best answers. Finally, a support vector machine-based chunker picks up some portions of the input sentence as NEs. This method introduces richer information to the chunker than previous methods that base on a single morphological analysis result. We apply our method to IREX NE extraction task. The cross validation result of the F-measure being 87.2 shows the superiority and effectiveness of the method.

Japanese Named Entity extraction with redundant morphological analysis

Recent work on temporal relation identification has focused on three types of relations between events: temporal relations between an event and a time expression, between a pair of events and between an event and the document creation time. These types of relations have mostly been identified in isolation by event pairwise comparison. However, this approach neglects logical constraints between temporal relations of different types that we believe to be helpful. We therefore propose a Markov Logic model that jointly identifies relations of all three relation types simultaneously. By evaluating our model on the TempEval data we show that this approach leads to about 2% higher accuracy for all three types of relations ---and to the best results for the task when compared to those of other machine learning based systems.

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Jointly Identifying Temporal Relations with Markov Logic

Statistical part-of-speech (POS) taggers achieve high accuracy and robustness when based on large scale manually tagged corpora. However, enhancements of the learning models are necessary to achieve better performance. We are developing a learning tool for a Japanese morphological analyzer called ChaSen. Currently we use a fine-grained POS tag set with about 500 tags. To apply a normal tri gram model on the tag set, we need unrealistic size of corpora. Even, for a bi-gram model, we cannot prepare a moderate size of an annotated corpus, when we take all the tags as distinct. A usual technique to cope with such fine-grained tags is to reduce the size of the tag set by grouping the set of tags into equivalence classes. We introduce the concept of position-wise grouping where the tag set is partitioned into different equivalence classes at each position in the conditional probabilities in the Markov Model. Moreover, to cope with the data sparseness problem caused by exceptional phenomena, we introduce several other techniques such as word-level statistics, smoothing of word-level and POS-level statistics and a selective tri-gram model. To help users determine probabilistic parameters, we introduce an error-driven method for the parameter selection. We then give results of experiments to see the effect of the tools applied to an existing Japanese morphological analyzer.

Extended Models and Tools for High-performance Part-of-speech

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Extended models and tools for high-performance part-of-speech tagger

This paper presents a method for categorizing named entities in Wikipedia. In Wikipedia, an anchor text is glossed in a linked HTML text. We formalize named entity categorization as a task of categorizing anchor texts with linked HTML texts which glosses a named entity. Using this representation, we introduce a graph structure in which anchor texts are regarded as nodes. In order to incorporate HTML structure on the graph, three types of cliques are defined based on the HTML tree structure. We propose a method with Conditional Random Fields (CRFs) to categorize the nodes on the graph. Since the defined graph may include cycles, the exact inference of CRFs is computationally expensive. We introduce an approximate inference method using Treebased Reparameterization (TRP) to reduce computational cost. In experiments, our proposed model obtained significant improvements compare to baseline models that use Support Vector Machines.

/pdf/a-graph-based-approach-to-named-entity-categorization-in-2cbtjb33td.pdf

Masayuki Asahara

Papers

Japanese Named Entity extraction with redundant morphological analysis

Jointly Identifying Temporal Relations with Markov Logic

Extended Models and Tools for High-performance Part-of-speech

Extended models and tools for high-performance part-of-speech tagger

A Graph-Based Approach to Named Entity Categorization in Wikipedia Using Conditional Random Fields