GreekLex 2: a comprehensive lexical database with part-of-speech, syllabic, phonological, and stress information

Databases containing lexical properties on any given orthography are crucial for psycholinguistic research. In the last ten years, a number of lexical databases have been developed for Greek. However, these lack important part-of-speech information. Furthermore, the need for alternative procedures for calculating syllabic measurements and stress information, as well as combination of several metrics to investigate linguistic properties of the Greek language are highlighted. To address these issues, we present a new extensive lexical database of Modern Greek (GreekLex 2) with part-of-speech information for each word and accurate syllabification and orthographic information predictive of stress, as well as several measurements of word similarity and phonetic information. The addition of detailed statistical information about Greek part-of-speech, syllabification, and stress neighbourhood allowed novel analyses of stress distribution within different grammatical categories and syllabic lengths to be carried out. Results showed that the statistical preponderance of stress position on the pre-final syllable that is reported for Greek language is dependent upon grammatical category. Additionally, analyses showed that a proportion higher than 90% of the tokens in the database would be stressed correctly solely by relying on stress neighbourhood information. The database and the scripts for orthographic and phonological syllabification as well as phonetic transcription are available at http://www.psychology.nottingham.ac.uk/greeklex/

Morphological Analysis as Classification: an Inductive-Learning Approach

Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast we present an inductive learning approach in which morphological analysis is reformulated as a segmentation task. We report on a number of experiments in which five inductive learning algorithms are applied to three variations of the task of morphological analysis. Results show (i) that the generalisation performance of the algorithms is good, and (ii) that the lazy learning algorithm IB1-IG performs best on all three tasks. We conclude that lazy learning of morphological analysis as a classification task is indeed a viable approach; moreover, it has the strong advantages over the traditional approach of avoiding the knowledge-acquisition bottleneck, being fast and deterministic in learning and processing, and being language-independent.Comment: 11 pages, 5 encapsulated postscript figures, uses non-standard NeMLaP proceedings style nemlap.sty; inputs ipamacs (international phonetic alphabet) and epsf macro

Memory-Based Lexical Acquisition and Processing

Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performance-oriented approach to Natural Language Processing based on automatic memory-based learning of linguistic (lexical) tasks. The consequences of the approach for computational lexicology are discussed, and the application of the approach on a number of lexical acquisition and disambiguation tasks in phonology, morphology and syntax is described.Comment: 18 page

A Tonal Grammar of Kere (Papuan) in Typological Perspective.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Automatic prosodic analysis for computer aided pronunciation teaching

Correct pronunciation of spoken language requires the appropriate modulation of acoustic characteristics of speech to convey linguistic information at a suprasegmental level. Such prosodic modulation is a key aspect of spoken language and is an important component of foreign language learning, for purposes of both comprehension and intelligibility. Computer aided pronunciation teaching involves automatic analysis of the speech of a non-native talker in order to provide a diagnosis of the learner's performance in comparison with the speech of a native talker. This thesis describes research undertaken to automatically analyse the prosodic aspects of speech for computer aided pronunciation teaching. It is necessary to describe the suprasegmental composition of a learner's speech in order to characterise significant deviations from a native-like prosody, and to offer some kind of corrective diagnosis. Phonological theories of prosody aim to describe the suprasegmental composition of speech..

vocoids and their prosodic distribution, with special reference to Italian and Arabic

This study attempts to characterize vocoids, i.e. vowels and semivowels, as a unified class of segments. In order to do so, it investigates the main phenomena concerning the quantitative distribution of these sounds, namely syllabic alternation, length alternations, deletion and insertion. Such phenomena are best analyzed by making reference to prosodic structure, and syllable structure in particular. Therefore, both frameworks adopted in this thesis take into consideration this type of representation. The main approach, which I refer to generally as Derivational Theory (DT), is based on the notion that surface phonetic forms are derived from underlying forms through a series of structural changes taking place at different levels of representation. This model is contrasted with the recently introduced (Prince and Smolensky 1993) Optimality Theory (OT), an output-oriented paradigm based on the parallel evaluation of candidate forms by means of universal but violable constraints. This thesis shows that OT offers some valuable insights into the phenomena under analysis, although there are areas in which it requires integration with derivational tools. This study also makes specific reference to two languages: Ammani Arabic and Standard Italian. These diverge in their treatment of vocoids, but clear general trends may be detected which have also been found in other languages

Automatic syllabification using segmental conditional random fields

In this paper we present a statistical approach for the automatic syllabification of phonetic word transcriptions. A syllable bigram language model forms the core of the system. Given the large number of syllables in non-syllabic languages, sparsity is the main issue, especially since the available syllabified corpora tend to be small. Traditional back-off mechanisms only give a partial solution to the sparsity problem. In this work we use a set of features for back-off purposes: on the one hand probabilities such as consonant cluster probabilities, and on the other hand a set of rules based on generic syllabification principles such as legality, sonority and maximal onset. For the combination of these highly correlated features with the baseline bigram feature we employ segmental conditional random fields (SCRFs) as statistical framework. The resulting method is very versatile and can be used for any amount of data of any language. The method was tested on various datasets in English and Dutch with dictionary sizes varying between 1 and 60 thousand words. We obtained a 97.96% word accuracy for supervised syllabification and a 91.22% word accuracy for unsupervised syllabification for English. When including the top-2 generated syllabifications for a small fraction of the words, virtual perfect syllabification is obtained in supervised mode

Syllable-based constraints on properties of English sounds

Also issued as Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.Includes bibliographical references (p. 169-174).Work sponsored in part by the Office of Naval Research. N00014-82-K-0727Mark A. Randolph