A computational approach to Zulu verb morphology within the context of lexical semantics

The central research question that is addressed in this article is: How can ZulMorph, a finite state morphological analyser for Zulu, be employed to add value to Zulu lexical semantics with specific reference to Zulu verbs? The verb is the most complex word category in Zulu. Due to the agglutinative nature of Zulu morphology, limited information can be computationally extracted from running Zulu text without the support of  sufficiently reliable computational mor-phological analysis by means of which the  essential meanings of, amongst others, verbs can be exposed. In this article we describe a corpus-based approach to adding the English meaning to Zulu extended verb roots, thereby enhancing ZulMorph as a lexical knowledge base.Keywords: Zulu Verb Morphology, Verb Extensions, Lexical Semantics, Computational Morphological Analysis, Zulmorph, Zulu Lexical Knowl-Edge Base, Bitext

Measuring the Semantic Specificity in Mandarin Verbs: A Corpus-based Quantitative Survey

[[abstract]]The purpose of this thesis is to study semantic specificity in Chinese based on corpus-based statistical and computational methods. The analysis begins with single verbs and does primitive tests with resultative verb compounds in Chinese. The verbs studied in this work include one hundred and fifty head verbs collected in the M3 project. As a prerequisite, these one hundred and fifty head verbs were tagged as generic or specific type following the three criteria proposed in literatures: the specification of agent/instrument, the limitation of objects and their types, and the confinement on the action denotation to only physical action. The next step is to measure semantic specificity with quantitative data. To specify the use of verbs by statistics, it relies on counting the frequency, the number of senses of a verb and the range of co-occurrence objects. Two major analyses, Principle Component Analysis (PCA) and Multinomial Logistic Model, are adopted to assess the predictive power of variables and to predict the probability of different verb categories. In addition, the vector-based model in Latent Semantic Analysis (LSA) is applied to justify the concept of semantic specificity. A distributional model based on Academia Sinica Balanced Corpus (ASBC) with LSA is built to investigate the semantic space variation depending on the semantic specificity. By measuring the vector distance, the semantic similarity between words is calculated. The word-space model is used to measure the semantic loads of single verbs and explore the semantic information on Chinese resultative verb compounds (RVCs).

Dictionary writing system (DWS) plus corpus query package (CQP): the case of TshwaneLex

In this article the integrated corpus query functionality of the dictionary compilation software TshwanelLex is analysed. Attention is given to the handling of both raw corpus data and annotated corpus data. With regard to the latter it is shown how, with a minimum of human effort, machine learning techniques can be employed to obtain part-of-speech tagged corpora that can be used for lexicographic purposes. All points are illustrated with data drawn from English and Northern Sotho. The tools and techniques themselves, however, are language-independent, and as Such the encouraging outcomes of this study are far-reaching

Word Knowledge and Word Usage

Word storage and processing define a multi-factorial domain of scientific inquiry whose thorough investigation goes well beyond the boundaries of traditional disciplinary taxonomies, to require synergic integration of a wide range of methods, techniques and empirical and experimental findings. The present book intends to approach a few central issues concerning the organization, structure and functioning of the Mental Lexicon, by asking domain experts to look at common, central topics from complementary standpoints, and discuss the advantages of developing converging perspectives. The book will explore the connections between computational and algorithmic models of the mental lexicon, word frequency distributions and information theoretical measures of word families, statistical correlations across psycho-linguistic and cognitive evidence, principles of machine learning and integrative brain models of word storage and processing. Main goal of the book will be to map out the landscape of future research in this area, to foster the development of interdisciplinary curricula and help single-domain specialists understand and address issues and questions as they are raised in other disciplines

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

Multilingual Lexicon Extraction under Resource-Poor Language Pairs

In general, bilingual and multilingual lexicons are important resources in many natural language processing fields such as information retrieval and machine translation. Such lexicons are usually extracted from bilingual (e.g., parallel or comparable) corpora with external seed dictionaries. However, few such corpora and bilingual seed dictionaries are publicly available for many language pairs such as Korean–French. It is important that such resources for these language pairs be publicly available or easily accessible when a monolingual resource is considered. This thesis presents efficient approaches for extracting bilingual single-/multi-word lexicons for resource-poor language pairs such as Korean–French and Korean–Spanish. The goal of this thesis is to present several efficient methods of extracting translated single-/multi-words from bilingual corpora based on a statistical method. Three approaches for single words and one approach for multi-words are proposed. The first approach is the pivot context-based approach (PCA). The PCA uses a pivot language to connect source and target languages. It builds context vectors from two parallel corpora sharing one pivot language and calculates their similarity scores to choose the best translation equivalents. The approach can reduce the effort required when using a seed dictionary for translation by using parallel corpora rather than comparable corpora. The second approach is the extended pivot context-based approach (EPCA). This approach gathers similar context vectors for each source word to augment its context. The approach assumes that similar vectors can enrich contexts. For example, young and youth can augment the context of baby. In the investigation described here, such similar vectors were collected by similarity measures such as cosine similarity. The third approach for single words uses a competitive neural network algorithm (i.e., self-organizing mapsSOM). The SOM-based approach (SA) uses synonym vectors rather than context vectors to train two different SOMs (i.e., source and target SOMs) in different ways. A source SOM is trained in an unsupervised way, while a target SOM is trained in a supervised way. The fourth approach is the constituent-based approach (CTA), which deals with multi-word expressions (MWEs). This approach reinforces the PCA for multi-words (PCAM). It extracts bilingual MWEs taking all constituents of the source MWEs into consideration. The PCAM 2 identifies MWE candidates by pointwise mutual information first and then adds them to input data as single units in order to use the PCA directly. The experimental results show that the proposed approaches generally perform well for resource-poor language pairs, particularly Korean and French–Spanish. The PCA and SA have demonstrated good performance for such language pairs. The EPCA would not have shown a stronger performance than expected. The CTA performs well even when word contexts are insufficient. Overall, the experimental results show that the CTA significantly outperforms the PCAM. In the future, homonyms (i.e., homographs such as lead or tear) should be considered. In particular, the domains of bilingual corpora should be identified. In addition, more parts of speech such as verbs, adjectives, or adverbs could be tested. In this thesis, only nouns are discussed for simplicity. Finally, thorough error analysis should also be conducted.Abstract List of Abbreviations List of Tables List of Figures Acknowledgement Chapter 1 Introduction 1.1 Multilingual Lexicon Extraction 1.2 Motivations and Goals 1.3 Organization Chapter 2 Background and Literature Review 2.1 Extraction of Bilingual Translations of Single-words 2.1.1 Context-based approach 2.1.2 Extended approach 2.1.3 Pivot-based approach 2.2 Extractiong of Bilingual Translations of Multi-Word Expressions 2.2.1 MWE identification 2.2.2 MWE alignment 2.3 Self-Organizing Maps 2.4 Evaluation Measures Chapter 3 Pivot Context-Based Approach 3.1 Concept of Pivot-Based Approach 3.2 Experiments 3.2.1 Resources 3.2.2 Results 3.3 Summary Chapter 4 Extended Pivot Context-Based Approach 4.1 Concept of Extended Pivot Context-Based Approach 4.2 Experiments 4.2.1 Resources 4.2.2 Results 4.3 Summary Chapter 5 SOM-Based Approach 5.1 Concept of SOM-Based Approach 5.2 Experiments 5.2.1 Resources 5.2.2 Results 5.3 Summary Chapter 6 Constituent-Based Approach 6.1 Concept of Constituent-Based Approach 6.2 Experiments 6.2.1 Resources 6.2.2 Results 6.3 Summary Chapter 7 Conclusions and Future Work 7.1 Conclusions 7.2 Future Work Reference

Multiword expressions at length and in depth

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work

Explaining Russian-German code-mixing

The study of grammatical variation in language mixing has been at the core of research into bilingual language practices. Although various motivations have been proposed in the literature to account for possible mixing patterns, some of them are either controversial, or remain untested. Little is still known about whether and how frequency of use of linguistic elements can contribute to the patterning of bilingual talk. This book is the first to systematically explore the factor usage frequency in a corpus of bilingual speech. The two aims are (i) to describe and analyze the variation in mixing patterns in the speech of Russia German adolescents and young adults in Germany, and (ii) to propose and test usage-based explanations of variation in mixing patterns in three morphosyntactic contexts: the adjective-modified noun phrase, the prepositional phrase, and the plural marking of German noun insertions in bilingual sentences. In these contexts, German noun insertions combine with either Russian or German words and grammatical markers, thus yielding mixed bilingual and German monolingual constituents in otherwise Russian sentences, the latter also labelled as embedded-language islands. The results suggest that the frequency with which words are used together mediates the distribution of mixing patterns in each of the examined contexts. The differing impacts of co-occurrence frequency are attributed to the distributional and semantic specifics of the analyzed morphosyntactic configurations. Lexical frequency has been found to be another important determinant in this variation. Other factors include recency, or lexical priming, in discourse in the case of prepositional phrases, and phonological and structural similarities and differences in the inflectional systems of the contact languages in the case of plural marking

Sequencing in SLA: Phonological Memory, Chunking and Points of Order.

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139863/1/Ellis1996Chunking.pd