A cross-language methodology for corpus part-of-speech tag-set development

This paper examines criteria used in development of Corpus Part-of-Speech tag sets used when PoS-tagging a corpus, that is, enriching a corpus by adding a part-of-speech category label to each word. This requires a tag-set, a list of grammatical category labels; a tagging scheme, practical definitions of each tag or label, showing words and contexts where each tag applies; and a tagger, a program for assigning a tag to each word in the corpus, implementing the tag-set and tagging-scheme in a tag-assignment algorithm

Comparing linguistic interpretation schemes for English corpora

Project AMALGAM explored a range of Partof-Speech tagsets and phrase structure parsing schemes used in modern English corpus-based research. The PoS-tagging schemes and parsing schemes include some which have been used for hand annotation of corpora or manual postediting of automatic taggers or parsers; and others which are unedited output of a parsing program. Project deliverables include: a detailed description of each PoS-tagging scheme, and multi-tagged corpus; a “Corpus-neutral ” tokenization scheme; a family of PoS-taggers, for 8 PoS-tagsets; a method for “PoS-tagset conversion”, a sample of texts parsed according to a range of parsing schemes: a MultiTreebank; an Internet service allowing researchers worldwide free access to the above resources, including a simple email-based method for PoS-tagging any English text with any or all PoS-tagset(s). We conclude that the range of tagging and parsing schemes in use is too varied to allow agreement on a standard; and that parserevaluation based on ‘bracket-matching ’ is unfair to more sophisticated parsers

A corpus for interstellar communication

Introduction: SETI, the Search for Extra-Terrestrial Intelligence Many researchers in Astronomy and Astronautics believe the Search for ExtraTerrestrial Intelligence is a serious academic enterprise, worthy of scholarly research and publication (e.g. Burke-Ward 2000, Couper and Henbest 1998, Day 1998, McDonough 1987, Sivier 2000, Norris 1999), and large-scale research sponsorship attracted by the SETI Institute in California. Most of this research community is focussed on techniques for detection of possible incoming signals from extraterrestrial intelligent sources (e.g. Turnbull et al 1999), and algorithms for analysis of these signals to identify intelligent language-like characteristics (e.g. Elliott and Atwell 1999, 2000). However, recently debate has turned to the nature of our response, should a signal arrive and be detected. For example, the 50th International Astronautical Congress devoted a full afternoon session to the question of whether and how we should respon

Joint Alignment of Segmentation and Labelling for Arabic Morphosyntactic Taggers

We present and compare three methods of alignment between morphemes resulting from four different Arabic POS - taggers as well as one baseline method using only provided labels. We combined four Arabic POS - taggers: MADAMIRA (M A), Stanford Tagger (ST), AMIRA (AM), Farasa (FA); and as the target output used two Classical Arabic gold standards: Quranic Arabic Corpus (QAC) and SALMA Standard Arabic Linguistics Morphological Analysis (SAL). We justify why we opt to use label for aligning instead of word form. The problem is not trivial as it is tackling six different tokenisation and labelling standards. The supervised learning using a unigram model scored the best segment alignment accuracy, correctly aligning 97 % of morpheme segments. We then evaluated the alignment methods extrinsically, in terms of their effect in improving accuracy of ensemble POS - taggers, merging different combinations of the four Arabic POS - taggers. Using the best approach to align input POS taggers, ensemble tagger has correctly segmented and tagged 88.09% of morphemes. We show how increasing the number of input taggers raise the accuracy, suggesting that input taggers make different errors

Corpus linguistics and language learning: bootstrapping linguistic knowledge and resources from text

This submission for the award of the degree of PhD by published work must: “make a contribution to knowledge in a coherent and related subject area; demonstrate originality and independent critical ability; satisfy the examiners that it is of sufficient merit to qualify for the award of the degree of PhD.” It includes a selection of my work as a Lecturer (and later, Senior Lecturer) at Leeds University, from 1984 to the present. The overall theme of my research has been bootstrapping linguistic knowledge and resources from text. A persistent strand of interest has been unsupervised and semi-supervised machine learning of linguistic knowledge from textual sources; the attraction of this approach is that I could start with English, but go on to apply analogous techniques to other languages, in particular Arabic. This theme covers a broad range of research over more than 20 years at Leeds University which I have divided into 8 sub-topics: A: Constituent-Likelihood statistical modelling of English grammar; B: Machine Learning of grammatical patterns from a corpus; C: Detecting grammatical errors in English text; D: Evaluation of English grammatical annotation models; E: Machine Learning of semantic language models; F: Applications in English language teaching; G: Arabic corpus linguistics; H: Applications in Computing teaching and research. The first section builds on my early years as a lecturer at Leeds University, when my research was essentially a progression from my previous work at Lancaster University on the LOB Corpus Part-of-Speech Tagging project (which resulted in the Tagged LOB Corpus, a resource for Corpus Linguistics research still in use today); I investigated a range of ideas for extending and/or applying techniques related to Part-of-Speech tagging in Corpus Linguistics. The second section covers a range of co-authored papers representing grant-funded research projects in Corpus Linguistics; in this mode of research, I had to come up with the original ideas and guide the project, but much of the detailed implementation was down to research assistant staff. Another highly productive mode of research has been supervision of research students, leading to further jointly-authored research papers. I helped formulate the research plans, and guided and advised the students; as with research-grant projects, the detailed implementation of the research has been down to the research students. The third section includes a few of the most significant of these jointly-authored Corpus Linguistics research papers. A “standard” PhD generally includes a survey of the field to put the work in context; so as a fourth section, I include some survey papers aimed at introducing new developments in corpus linguistics to a wider audience

Automatic Extraction of Tagset Mappings from Parallel-Annotated Corpora

Several research projects around the world are building grammatically analysed corpora; that is, collections of text annotated with part-of-speech wordtags and syntax trees. However, projects have used quite different wordtagging and parsing schemes. Developers of corpora adhere to a variety of competing models or theories of grammar and parsing, with the effect of restricting the accessibility of their respective corpora, and the potential for collation into a single fully parsed corpus. In view of this heterogeneity, we have begun to investigate and develop methods of automatically mapping between the annotation schemes of the most widely known corpora, thus assessing their differences and improving their reusability. Annotating a single corpus with the different schemes allows for comparisons and will provide a rich testbed for automatic parsers. Collation of all the included corpora into a single large annotated corpus will provide a more detailed language model to be developed for..

Ensemble Morphosyntactic Analyser for Classical Arabic

Classical Arabic (CA) is an influential language for Muslim lives around the world. It is the language of two sources of Islamic laws: the Quran and the Sunnah, the collection of traditions and sayings attributed to the prophet Mohammed. However, classical Arabic in general, and the Sunnah, in particular, is underexplored and under-resourced in the field of computational linguistics. This study examines the possible directions for adapting existing tools, specifically morphological analysers, designed for modern standard Arabic (MSA) to classical Arabic. Morphological analysers of CA are limited, as well as the data for evaluating them. In this study, we adapt existing analysers and create a validation data-set from the Sunnah books. Inspired by the advances in deep learning and the promising results of ensemble methods, we developed a systematic method for transferring morphological analysis that is capable of handling different labelling systems and various sequence lengths. In this study, we handpicked the best four open access MSA morphological analysers. Data generated from these analysers are evaluated before and after adaptation through the existing Quranic Corpus and the Sunnah Arabic Corpus. The findings are as follows: first, it is feasible to analyse under-resourced languages using existing comparable language resources given a small sufficient set of annotated text. Second, analysers typically generate different errors and this could be exploited. Third, an explicit alignment of sequences and the mapping of labels is not necessary to achieve comparable accuracies given a sufficient size of training dataset. Adapting existing tools is easier than creating tools from scratch. The resulting quality is dependent on training data size and number and quality of input taggers. Pipeline architecture performs less well than the End-to-End neural network architecture due to error propagation and limitation on the output format. A valuable tool and data for annotating classical Arabic is made freely available