Methods and algorithms for unsupervised learning of morphology

This is an accepted manuscript of a chapter published by Springer in Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403 in 2014 available online: https://doi.org/10.1007/978-3-642-54906-9_15 The accepted version of the publication may differ from the final published version.This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL (minimum description length), MLE (maximum likelihood estimation), MAP (maximum a posteriori), parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.Published versio

Proceedings of the Morpho Challenge 2010 Workshop

In natural language processing many practical tasks, such as speech recognition, information retrieval and machine translation depend on a large vocabulary and statistical language models. For morphologically rich languages, such as Finnish and Turkish, the construction of a vocabulary and language models that have a sufficient coverage is particularly difficult, because of the huge amount of different word forms. In Morpho Challenge 2010 unsupervised and semi-supervised algorithms are suggested to provide morpheme analyses for words in different languages and evaluated in various practical applications. As a research theme, unsupervised morphological analysis has received wide attention in conferences and scientific journals focused on computational linguistic and its applications. This is the proceedings of the Morpho Challenge 2010 Workshop that contains one introduction article with a description of the tasks, evaluation and results and six articles describing the participating unsupervised and supervised learning algorithms. The Morpho Challenge 2010 Workshop was held at Espoo, Finland in 2-3 September, 2010.reviewe

A Survey and Classification of Methods for (Mostly) Unsupervised Learning

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 292-296

Resource Generation from Structured Documents for Low-density Languages

The availability and use of electronic resources for both manual and automated language related processing has increased tremendously in recent years. Nevertheless, many resources still exist only in printed form, restricting their availability and use. This especially holds true in low density languages or languages with limited electronic resources. For these documents, automated conversion into electronic resources is highly desirable. This thesis focuses on the semi-automated conversion of printed structured documents (dictionaries in particular) to usable electronic representations. In the first part we present an entry tagging system that recognizes, parses, and tags the entries of a printed dictionary to reproduce the representation. The system uses the consistent layout and structure of the dictionaries, and the features that impose this structure, to capture and recover lexicographic information. We accomplish this by adapting two methods: rule-based and HMM-based. The system is designed to produce results quickly with minimal human assistance and reasonable accuracy. The use of an adaptive transformation-based learning as a post-processor at two points in the system yields significant improvements, even with an extremely small amount of user provided training data. The second part of this thesis presents Morphology Induction from Noisy Data (MIND), a natural language morphology discovery framework that operates on information from limited, noisy data obtained from the conversion process. To use the resulting resources effectively, however, users must be able to search for them using the root form of morphologically deformed variant found in the text. Stemming and data driven methods are not suitable when data are sparse. The approach is based on the novel application of string searching algorithms. The evaluations show that MIND can segment words into roots and affixes from the noisy, limited data contained in a dictionary, and it can extract prefixes, suffixes, circumfixes, and infixes. MIND can also identify morphophonemic changes, i.e., phonemic variations between allomorphs of a morpheme, specifically point-of-affixation stem changes. This, in turn, allows non-native speakers to perform multilingual tasks for applications where response must be rapid, and they have limited knowledge. In addition, this analysis can feed other natural language processing tools requiring lexicons

Induction of the morphology of natural language : unsupervised morpheme segmentation with application to automatic speech recognition

In order to develop computer applications that successfully process natural language data (text and speech), one needs good models of the vocabulary and grammar of as many languages as possible. According to standard linguistic theory, words consist of morphemes, which are the smallest individually meaningful elements in a language. Since an immense number of word forms can be constructed by combining a limited set of morphemes, the capability of understanding and producing new word forms depends on knowing which morphemes are involved (e.g., "water, water+s, water+y, water+less, water+less+ness, sea+water"). Morpheme boundaries are not normally marked in text unless they coincide with word boundaries. The main objective of this thesis is to devise a method that discovers the likely locations of the morpheme boundaries in words of any language. The method proposed, called Morfessor, learns a simple model of concatenative morphology (word forming) in an unsupervised manner from plain text. Morfessor is formulated as a Bayesian, probabilistic model. That is, it does not rely on predefined grammatical rules of the language, but makes use of statistical properties of the input text. Morfessor situates itself between two types of existing unsupervised methods: morphology learning vs. word segmentation algorithms. In contrast to existing morphology learning algorithms, Morfessor can handle words consisting of a varying and possibly high number of morphemes. This is a requirement for coping with highly-inflecting and compounding languages, such as Finnish. In contrast to existing word segmentation methods, Morfessor learns a simple grammar that takes into account sequential dependencies, which improves the quality of the proposed segmentations. Morfessor is evaluated in two complementary ways in this work: directly by comparing to linguistic reference morpheme segmentations of Finnish and English words and indirectly as a component of a large (or virtually unlimited) vocabulary Finnish speech recognition system. In both cases, Morfessor is shown to outperform state-of-the-art solutions. The linguistic reference segmentations were produced as part of the current work, based on existing linguistic resources. This has resulted in a morphological gold standard, called Hutmegs, containing analyses of a large number of Finnish and English word forms.reviewe

Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon

Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole-word model, or a model that seeks for an optimal balance between these two extremes. Our results showed that human word recognition was predicted best by a combination of two models: a model that decomposes words at some morpheme boundaries while keeping others unsegmented and a whole-word model. The results support dual-route models that assume that both decomposed and full-form representations are utilized to optimally process complex words within the mental lexicon.Peer reviewe

Turkish lexicon expansion by using finite state automata

© 2019 The Authors. Published by The Scientific and Technological Research Council of Turkey. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://journals.tubitak.gov.tr/elektrik/issues/elk-19-27-2/elk-27-2-25-1804-10.pdfTurkish is an agglutinative language with rich morphology. A Turkish verb can have thousands of different word forms. Therefore, sparsity becomes an issue in many Turkish natural language processing (NLP) applications. This article presents a model for Turkish lexicon expansion. We aimed to expand the lexicon by using a morphological segmentation system by reversing the segmentation task into a generation task. Our model uses finite-state automata (FSA) to incorporate orthographic features and morphotactic rules. We extracted orthographic features by capturing phonological operations that are applied to words whenever a suffix is added. Each FSA state corresponds to either a stem or a suffix category. Stems are clustered based on their parts-of-speech (i.e. noun, verb, or adjective) and suffixes are clustered based on their allomorphic features. We generated approximately 1 million word forms by using only a few thousand Turkish stems with an accuracy of 82.36%, which will help to reduce the out-of-vocabulary size in other NLP applications. Although our experiments are performed on Turkish language, the same model is also applicable to other agglutinative languages such as Hungarian and Finnish.Published versio

Probabilistic Modelling of Morphologically Rich Languages

This thesis investigates how the sub-structure of words can be accounted for in probabilistic models of language. Such models play an important role in natural language processing tasks such as translation or speech recognition, but often rely on the simplistic assumption that words are opaque symbols. This assumption does not fit morphologically complex language well, where words can have rich internal structure and sub-word elements are shared across distinct word forms. Our approach is to encode basic notions of morphology into the assumptions of three different types of language models, with the intention that leveraging shared sub-word structure can improve model performance and help overcome data sparsity that arises from morphological processes. In the context of n-gram language modelling, we formulate a new Bayesian model that relies on the decomposition of compound words to attain better smoothing, and we develop a new distributed language model that learns vector representations of morphemes and leverages them to link together morphologically related words. In both cases, we show that accounting for word sub-structure improves the models' intrinsic performance and provides benefits when applied to other tasks, including machine translation. We then shift the focus beyond the modelling of word sequences and consider models that automatically learn what the sub-word elements of a given language are, given an unannotated list of words. We formulate a novel model that can learn discontiguous morphemes in addition to the more conventional contiguous morphemes that most previous models are limited to. This approach is demonstrated on Semitic languages, and we find that modelling discontiguous sub-word structures leads to improvements in the task of segmenting words into their contiguous morphemes.Comment: DPhil thesis, University of Oxford, submitted and accepted 2014. http://ora.ox.ac.uk/objects/uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c

Advances in Weakly Supervised Learning of Morphology

Morphological analysis provides a decomposition of words into smaller constituents. It is an important problem in natural language processing (NLP), particularly for morphologically rich languages whose large vocabularies make statistical modeling difficult. Morphological analysis has traditionally been approached with rule-based methods that yield accurate results, but are expensive to produce. More recently, unsupervised machine learning methods have been shown to perform sufficiently well to benefit applications such as speech recognition and machine translation. Unsupervised methods, however, do not typically model allomorphy, that is, non-concatenative structure, for example pretty/prettier. Moreover, the accuracy of unsupervised methods remains far behind rule-based methods with the best unsupervised methods yielding between 50-66% F-score in Morpho Challenge 2010. We examine these problems with two approaches that have not previously attracted much attention in the field. First, we propose a novel extension to the popular unsupervised morphological segmentation method Morfessor Baseline to model allomorphy via the use of string transformations. Second, we examine the effect of weak supervision on accuracy by training on a small annotated data set in addition to a large unannotated data set. We propose two novel semi-supervised morphological segmentation methods, namely a semi-supervised extension of Morfessor Baseline and morphological segmentation with conditional random fields (CRF). The methods are evaluated on several languages with different morphological characteristics, including English, Estonian, Finnish, German and Turkish. The proposed methods are compared empirically to recently proposed weakly supervised methods. For the non-concatenative extension, we find that, while the string transformations identified by the model have high precision, their recall is low. In the overall evaluation the non-concatenative extension improves accuracy on English, but not on other languages. For the weak supervision we find that the semi-supervised extension of Morfessor Baseline improves the accuracy of segmentation markedly over the unsupervised baseline. We find, however, that the discriminatively trained CRFs perform even better. In the empirical comparison, the CRF approach outperforms all other approaches on all included languages. Error analysis reveals that the CRF excels especially on affix accuracy