Minimally supervised induction of morphology through bitexts

textA knowledge of morphology can be useful for many natural language processing systems. Thus, much effort has been expended in developing accurate computational tools for morphology that lemmatize, segment and generate new forms. The most powerful and accurate of these have been manually encoded, such endeavors being without exception expensive and time-consuming. There have been consequently many attempts to reduce this cost in the development of morphological systems through the development of unsupervised or minimally supervised algorithms and learning methods for acquisition of morphology. These efforts have yet to produce a tool that approaches the performance of manually encoded systems. Here, I present a strategy for dealing with morphological clustering and segmentation in a minimally supervised manner but one that will be more linguistically informed than previous unsupervised approaches. That is, this study will attempt to induce clusters of words from an unannotated text that are inflectional variants of each other. Then a set of inflectional suffixes by part-of-speech will be induced from these clusters. This level of detail is made possible by a method known as alignment and transfer (AT), among other names, an approach that uses aligned bitexts to transfer linguistic resources developed for one language–the source language–to another language–the target. This approach has a further advantage in that it allows a reduction in the amount of training data without a significant degradation in performance making it useful in applications targeted at data collected from endangered languages. In the current study, however, I use English as the source and German as the target for ease of evaluation and for certain typlogical properties of German. The two main tasks, that of clustering and segmentation, are approached as sequential tasks with the clustering informing the segmentation to allow for greater accuracy in morphological analysis. While the performance of these methods does not exceed the current roster of unsupervised or minimally supervised approaches to morphology acquisition, it attempts to integrate more learning methods than previous studies. Furthermore, it attempts to learn inflectional morphology as opposed to derivational morphology, which is a crucial distinction in linguistics.Linguistic

Advances in unlimited-vocabulary speech recognition for morphologically rich languages

Automatic speech recognition systems are devices or computer programs that convert human speech into text or make actions based on what is said to the system. Typical applications include dictation, automatic transcription of large audio or video databases, speech-controlled user interfaces, and automated telephone services, for example. If the recognition system is not limited to a certain topic and vocabulary, covering the words in the target languages as well as possible while maintaining a high recognition accuracy becomes an issue. The conventional way to model the target language, especially in English recognition systems, is to limit the recognition to the most common words of the language. A vocabulary of 60 000 words is usually enough to cover the language adequately for arbitrary topics. On the other hand, in morphologically rich languages, such as Finnish, Estonian and Turkish, long words can be formed by inflecting and compounding, which makes it difficult to cover the language adequately by vocabulary-based approaches. This thesis deals with methods that can be used to build efficient speech recognition systems for morphologically rich languages. Before training the statistical n-gram language models on a large text corpus, the words in the corpus are automatically segmented into smaller fragments, referred to as morphs. The morphs are then used as modelling units of the n-gram models instead of whole words. This makes it possible to train the model on the whole text corpus without limiting the vocabulary and enables the model to create even unseen words by joining morphs together. Since the segmentation algorithm is unsupervised and data-driven, it can be readily used for many languages. Speech recognition experiments are made on various Finnish recognition tasks and some of the experiments are also repeated on an Estonian task. It is shown that the morph-based language models reduce recognition errors when compared to word-based models. It seems to be important, however, that the n-gram models are allowed to use long morph contexts, especially if the morphs used by the model are short. This can be achieved by using growing and pruning algorithms to train variable-length n-gram models. The thesis also presents data structures that can be used for representing the variable-length n-gram models efficiently in recognition systems. By analysing the recognition errors made by Finnish recognition systems it is found out that speaker adaptive training and discriminative training methods help to reduce errors in different situations. The errors are also analysed according to word frequencies and manually defined error classes

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

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

On lexicon creation for turkish LVCSR

In this paper, we address the lexicon design problem in Turkish large vocabulary speech recognition. Although we focus only on Turkish, the methods described here are general enough that they can be considered for other agglutinative languages like Finnish, Korean etc. In an agglutinative language, several words can be created from a single root word using a rich collection of morphological rules. So, a virtually infinite size lexicon is required to cover the language if words are used as the basic units. The standard approach to this problem is to discover a number of primitive units so that a large set of words can be created by compounding those units. Two broad classes of methods are available for splitting words into their sub-units; morphology-based and data-driven methods. Although the word splitting significantly reduces the out of vocabulary rate, it shrinks the context and increases acoustic confusibility. We have used two methods to address the latter. In one method, we use word counts to avoid splitting of high frequency lexical units, and in the other method, we recompound splits according to a probabilistic measure. We present experimental results that show the methods are very effective to lower the word error rate at the expense of lexicon size

On Lexicon Creation for Turkish LVCSR

In this paper, we address the lexicon design problem in Turkish large vocabulary speech recognition. Although we focus only on Turkish, the methods described here are general enough that they can be considered for other agglutinative languages like Finnish, Korean etc. In an agglutinative language, several words can be created from a single root word using a rich collection of morphological rules. So, a virtually infinite size lexicon is required to cover the language if words are used as the basic units. The standard approach to this problem is to discover a number of primitive units so that a large set of words can be created by compounding those units. Two broad classes of methods are available for splitting words into their sub-units; morphology-based and data-driven methods. Although the word splitting significantly reduces the out of vocabulary rate, it shrinks the context and increases acoustic confusibility. We have used two methods to address the latter. In one method, we use word counts to avoid splitting of high frequency lexical units, and in the other method, we recompound splits according to a probabilistic measure. We present experimental results that show the methods are very effective to lower the word error rate at the expense of lexicon size. 1

On Lexicon Creation for Turkish LVCSR

In this paper, we address the lexicon design problem in Turkish large vocabulary speech recognition. Although we focus only on Turkish, the methods described here are general enough that they can be considered for other agglutinative languages like Finnish, Korean etc. In an agglutinative language, several words can be created from a single root word using a rich collection of morphological rules. So, a virtually infinite size lexicon is required to cover the language if words are used as the basic units. The standard approach to this problem is to discover a number of primitive units so that a large set of words can be created by compounding those units. Two broad classes of methods are available for splitting words into their sub-units; morphology-based and data-driven methods. Although the word splitting significantly reduces the out of vocabulary rate, it shrinks the context and increases acoustic confusibility. We have used two methods to address the latter. In one method, we use word counts to avoid splitting of high frequency lexical units, and in the other method, we recompound splits according to a probabilistic measure. We present experimental results that show the methods are very effective to lower the word error rate at the expense of lexicon size