: 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

/pdf/building-a-large-annotated-corpus-of-english-the-penn-4tcny82kaq.pdf

Building a large annotated corpus of English: the penn treebank

This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.

/pdf/generating-sequences-with-recurrent-neural-networks-i8ycdrmrq7.pdf

Generating Sequences With Recurrent Neural Networks

Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.

https://ebiquity.umbc.edu/_file_directory_/papers/369.pdf

Why we twitter: understanding microblogging usage and communities

Recurrent neural networks are powerful sequence learners. They are able to incorporate context information in a flexible way, and are robust to localised distortions of the input data. These properties make them well suited to sequence labelling, where input sequences are transcribed with streams of labels. The aim of this thesis is to advance the state-of-the-art in supervised sequence labelling with recurrent networks. Its two main contributions are (1) a new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and (2) an extension of the long short-term memory network architecture to multidimensional data, such as images and video sequences.

https://mediatum.ub.tum.de/doc/673554/file.pdf

Supervised Sequence Labelling with Recurrent Neural Networks

In this paper, we present a statistical approach to machine translation. We describe the application of our approach to translation from French to English and give preliminary results.

/pdf/a-statistical-approach-to-machine-translation-1edace65d2.pdf

A statistical approach to machine translation

This paper describes a method of comparing corpora which uses frequency profiling. The method can be used to discover key words in the corpora which differentiate one corpus from another. Using annotated corpora, it can be applied to discover key grammatical or word-sense categories. This can be used as a quick way in to find the differences between the corpora and is shown to have applications in the study of social differentiation in the use of English vocabulary, profiling of learner English and document analysis in the software engineering process.

/pdf/comparing-corpora-using-frequency-profiling-3bx6bs1617.pdf

Comparing Corpora using Frequency Profiling

List of contributors Preface 1. Introducing corpus annotation Geoffrey Leech 2. Grammatical tagging Geoffrey Leech 3. Syntactic annotation: treebanks Geoffrey Leech and Elizabeth Eyes 4. Semantic annotation Andrew Wilson and Jenny Thomas 5. Discourse annotation: anaphoric relations in corpora Roger Garside, Steve Fligelstone and Simon Botley 6. Further levels of annotation Geoffrey Leech, Anthony McEnery and Martin Wynne 7. A hybrid grammatical tagger: CLAWS4 Roger Garside and Nicholas Smith 8. How to generalise the task of annotation Steve Fligelstone, Mike Pacey and Paul Rayson 9. Improving a tagger Nicholas Smith 10. Retageting a tagger Fernando Sanchez Leon and Amalio F.Nieto-Serrano 11. The use of syntactic annotation tools: partial and full parsing Jeremy Bateman, Jean Forrest, and Tim Willis 12. Higher-level annotation tools Roger Garside and Paul Rayson 13. A corpus/annotation toolbox Anthony McEnery and Paul Rayson 14. A corpus-based grammar tool Anthony McEnery, John Paul Baker and John Hutchinson 15. The exploitation of multilingual annotated corpora for term extraction Anthony McEnery, Jean-Marc Lange, Michael Oakes and Jean Veronis 16. Cross-linguistic guidelines for the annotation of corpora Peter Kahrel, Ruthanna Barnett and Geoffrey Leech 17. Consistency and accuracy in correcting automatically-tagged corpora John Paul Baker Appendix I: Sources for further information (WWW and e-mail addresses) Appendix II: Abbreviations and acronyms Appendix III: Specimen annotation practices: the C7 and C5 tagsets Bibliography Index

Corpus Annotation: Linguistic Information from Computer Text Corpora

The Computational analysis of English : a corpus-based approach

The main purpose of this paper is to describe the CLAWS4 general-purpose grammatical tagger, used for the tagging of the 100-million-word British National Corpus, of which c.70 million words have been tagged at the time of writing (April 1994)) We will emphasise the goals of (a) gener~d-purpose adaptability, (b) incorporation of linguistic knowledge to improve quality ,and consistency, and (c) accuracy, measured consistently and in a linguistically informed way. The British National Corpus (BNC) consists of c.100 million words of English written texts and spoken transcriptions, sampled from a comprehensive range of text types. The BNC includes 10 million words of spoken h'mguage, c.45% of which is impromptu conversation (see Crowdy, forthcoming). It also includes ,an immense variety of written texts, including unpublished materials. The gr,'unmatical tagging of the corpus has therefore required the 'super-robustness' of a tagger which can adapt well to virtually all kinds of text. The tagger also has had to be versatile in dealing with different tagsets (sets of grammatical category labels-see 3 below) and accepting text in varied input formats. For the purposes of the BNC, l, he tagger has been requircd both to accept and to output text in a corpus-oriented TEl-confonnant mark-up definition known as CDIF (Corpus Document Interchange Format), but within this format many variant fornaats (affecting, for example, segmentation into words and sentences) can be readily accepted. In addition, CLAWS al-

/pdf/claws4-the-tagging-of-the-british-national-corpus-5et3d15419.pdf

Roger Garside

Papers

Comparing Corpora using Frequency Profiling

Corpus Annotation: Linguistic Information from Computer Text Corpora

The Computational analysis of English : a corpus-based approach

CLAWS4: the tagging of the British National Corpus

The tagged LOB Corpus : user's manual