A Case Study of Algorithms for Morphosyntactic Tagging of Polish Language

The paper presents an evaluation of several part-of-speech taggers, representing main tagging algorithms, applied to corpus of frequency dictionary of the contemporary Polish language. We report our results considering two tagging schemes: IPI PAN positional tagset and its simplified version. Tagging accuracy is calculated for different training sets and takes into account many subcategories (accuracy on known and unknown tokens, word segments, sentences etc.) The comparison of results with other inflecting and analytic languages is done. Performance aspects (time demands) of used tagging tools are also discussed

‘Taking an Interdisciplinary Approach to Designing the Theoretical Part of a Level 5 Initial Teacher Education Programme Based on the ‘Triple Professionalism’ Concept, for Adult Community Learning Teachers in Essex’

The Lingfield report (DBIS 2012) recommended that post-compulsory sector (PCS) institutions become business-oriented and revoked the statutory obligation for teachers to hold Level 5 (L5) teaching qualifications. As a result, the PCS, including my setting, Essex Adult Community Learning (EACL), has gone through unprecedented reforms. EACL offers adults recreational and career-enhancing education. EACL’s inherent complexity has challenged its teachers’ professional identity as most became teachers whilst or after pursuing another career, work on an ‘as and when’ basis and are not L5 teacher-trained. Practice demands have increased in response to local government and EACL restructures which gave EACL a significant role in delivering community development policy. My qualitative research project explores the views of EACL teachers and managers, an adult education expert, a leading adult community learning policy figure and one of the authors of Triple Professionalism (TP) which is a professionalism model which adds to dual professionalism the knowledges/skills needed for working interdependently with other community stakeholders. The research draws together reported elements of EACL teachers’ professionalism and examines the professional knowledges/skills required of the teachers’ new spheres of operation. It explores TP’s potential for supporting EACL teachers’ professional identity and informing three new centre-devised modules to be incorporated in the Education and Training Foundation’s L5 Diploma in Education and Training (DET). The research concludes with proposing the structure of an EACL-specific TP-DET and recommending the content of the centre-devised modules. The resulting TP-DET borrows from interdisciplinary methodologies because it is combining traditional teacher-training curriculum elements with knowledges/skills borrowed from unrelated-to-teaching fields, in particular, Learning Technology, Community Development and Business Management. Its delivery approach is also interdisciplinary because the field-expert lecturers join teacher-trainers in equipping trainees for classroom teaching whilst enabling them to understand and contribute to EACL’s managerial processes and business expansion and affirm their functionality as community workers. Current professional initial teacher education programmes do not afford the latter; the TP-DET, does

Application of Weighted Voting Taggers to Languages Described with Large Tagsets

The paper presents baseline and complex part-of-speech taggers applied to the modified corpus of Frequency Dictionary of Contemporary Polish, annotated with a large tagset. First, the paper examines accuracy of 6 baseline part-of-speech taggers. The main part of the work presents simple weighted voting and complex voting taggers. Special attention is paid to lexical voting methods and issues of ties and fallbacks. TagPair and WPDV voting methods achieve the top accuracy among all considered methods. Error reduction 10.8 % with respect to the best baseline tagger for the large tagset is comparable with other author's results for small tagsets

The Pragmatics of Word Meaning

In this paper, we explore the interaction between lexical semantics and pragmat­ics. Linguistic processing is nformationally encapsulated and utilises relatively simple 'taxonomic' lexical semantic knowledge. On this basis, defeasible lexical generalisations deliver defeasible parts of logical form. In contrast, pragmatics is open-ended and involves arbitrary knowledge. Two axioms specify when pragmatic defaults override lexical ones. We demonstrate that modelling this interaction al­lows us to achieve a more refined interpretation of words in a discourse context than either the lexicon or pragmatics could do on their own

Part-of-speech Tagging: A Machine Learning Approach based on Decision Trees

The study and application of general Machine Learning (ML) algorithms to theclassical ambiguity problems in the area of Natural Language Processing (NLP) isa currently very active area of research. This trend is sometimes called NaturalLanguage Learning. Within this framework, the present work explores the applicationof a concrete machine-learning technique, namely decision-tree induction, toa very basic NLP problem, namely part-of-speech disambiguation (POS tagging).Its main contributions fall in the NLP field, while topics appearing are addressedfrom the artificial intelligence perspective, rather from a linguistic point of view.A relevant property of the system we propose is the clear separation betweenthe acquisition of the language model and its application within a concrete disambiguationalgorithm, with the aim of constructing two components which are asindependent as possible. Such an approach has many advantages. For instance, thelanguage models obtained can be easily adapted into previously existing taggingformalisms; the two modules can be improved and extended separately; etc.As a first step, we have experimentally proven that decision trees (DT) providea flexible (by allowing a rich feature representation), efficient and compact wayfor acquiring, representing and accessing the information about POS ambiguities.In addition to that, DTs provide proper estimations of conditional probabilities fortags and words in their particular contexts. Additional machine learning techniques,based on the combination of classifiers, have been applied to address some particularweaknesses of our tree-based approach, and to further improve the accuracy in themost difficult cases.As a second step, the acquired models have been used to construct simple,accurate and effective taggers, based on diiferent paradigms. In particular, wepresent three different taggers that include the tree-based models: RTT, STT, andRELAX, which have shown different properties regarding speed, flexibility, accuracy,etc. The idea is that the particular user needs and environment will define whichis the most appropriate tagger in each situation. Although we have observed slightdifferences, the accuracy results for the three taggers, tested on the WSJ test benchcorpus, are uniformly very high, and, if not better, they are at least as good asthose of a number of current taggers based on automatic acquisition (a qualitativecomparison with the most relevant current work is also reported.Additionally, our approach has been adapted to annotate a general Spanishcorpus, with the particular limitation of learning from small training sets. A newtechnique, based on tagger combination and bootstrapping, has been proposed toaddress this problem and to improve accuracy. Experimental results showed thatvery high accuracy is possible for Spanish tagging, with a relatively low manualeffort. Additionally, the success in this real application has confirmed the validity of our approach, and the validity of the previously presented portability argumentin favour of automatically acquired taggers

The New Hampshire, Vol. 79, No. 22 (Nov. 18, 1988)

The student publication of the University of New Hampshire