We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.

/pdf/conditional-random-fields-probabilistic-models-for-qeo4l25dzg.pdf

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

https://ssli.ee.washington.edu/people/duh/projects/CRFintro.pdf

Probabilistic Models for Segmenting and Labeling Sequence Data

: As a result of this grant, the researchers have now published oil CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, with over 3 million words of that material assigned skeletal grammatical structure This material now includes a fully hand-parsed version of the classic Brown corpus About one half of the papers at the ACL Workshop on Using Large Text Corpora this past summer were based on the materials generated by this grant

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Building a large annotated corpus of English: the penn treebank

We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.

/pdf/moses-open-source-toolkit-for-statistical-machine-3pmfuwk8uc.pdf

Moses: Open Source Toolkit for Statistical Machine Translation

SRILM is a collection of C++ libraries, executable programs, and helper scripts designed to allow both production of and experimentation with statistical language models for speech recognition and other applications. SRILM is freely available for noncommercial purposes. The toolkit supports creation and evaluation of a variety of language model types based on N-gram statistics, as well as several related tasks, such as statistical tagging and manipulation of N-best lists and word lattices. This paper summarizes the functionality of the toolkit and discusses its design and implementation, highlighting ease of rapid prototyping, reusability, and combinability of tools.

SRILM – An Extensible Language Modeling Toolkit

Recently, there has been a rebirth of empiricism in the field of natural language processing. Manual encoding of linguistic information is being challenged by automated corpus-based learning as a method of providing a natural language processing system with linguistic knowledge. Although corpus-based approaches have been successful in many different areas of natural language processing, it is often the case that these methods capture the linguistic information they are modelling indirectly in large opaque tables of statistics. This can make it difficult to analyze, understand and improve the ability of these approaches to model underlying linguistic behavior. In this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learning method applied to part-of-speech tagging.

https://faculty.washington.edu/fxia/courses/LING572/Brill_CL95.pdf

Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule-based methods. In this paper, we present a simple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy comparable to stochastic taggers. The rule-based tagger has many advantages over these taggers, including: a vast reduction in stored information required, the perspicuity of a small set of meaningful rules, ease of finding and implementing improvements to the tagger, and better portability from one tag set, corpus genre or language to another. Perhaps the biggest contribution of this work is in demonstrating that the stochastic method is not the only viable method for part of speech tagging. The fact that a simple rule-based tagger that automatically learns its rules can perform so well should offer encouragement for researchers to further explore rule-based tagging, searching for a better and more expressive set of rule templates and other variations on the simple but effective theme described below.

/pdf/a-simple-rule-based-part-of-speech-tagger-qzu9bs0sgz.pdf

A Simple Rule-Based Part of Speech Tagger

We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.

/pdf/improving-web-search-ranking-by-incorporating-user-behavior-3iogacsa1k.pdf

Improving web search ranking by incorporating user behavior information

Finding consensus in speech recognition: word error minimization and other applications of confusion networks☆

The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. In this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. We are fortunate that for this particular application, correctly labeled training data is free. Since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.

/pdf/scaling-to-very-very-large-corpora-for-natural-language-1g8ymi38u8.pdf

Eric D. Brill

Papers

Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

A Simple Rule-Based Part of Speech Tagger

Improving web search ranking by incorporating user behavior information

Finding consensus in speech recognition: word error minimization and other applications of confusion networks☆

Scaling to Very Very Large Corpora for Natural Language Disambiguation