Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer (https://lingualyzer.com), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface

Large-alphabet sequence modelling - a comparative study

Most raw data is not binary, but over some often large and structured alphabet. Sometimes it is convenient to deal with binarised data sequence, but typically exploiting the original structure of the data significantly improves performance in many practical applications. In this thesis, we study Martin-Lof random sequences that are maximally incompressible and provide a topological view on the size of the set of random sequences. We also investigate the relationship between binary data compression techniques and modelling natural language text with the latter using raw unbinarised data sequence from a large alphabet. We perform an experimental comparative study for them, including an empirical comparison between Kneser-Ney (KN) variants with regular Context Tree Weighting algorithm (CTW) and phase CTW, and with large-alphabet CTW with different estimators. We also apply the idea of Hutter's adaptive sparse Dirichlet-multinomial coding to the KN method and provide a heuristic to make the discounting parameter adaptive. The KN with this adaptive discounting parameter outperforms the traditional KN method on the Large Calgary corpus

Finding structure in language

Since the Chomskian revolution, it has become apparent that natural language is richly structured, being naturally represented hierarchically, and requiring complex context sensitive rules to define regularities over these representations. It is widely assumed that the richness of the posited structure has strong nativist implications for mechanisms which might learn natural language, since it seemed unlikely that such structures could be derived directly from the observation of linguistic data (Chomsky 1965).This thesis investigates the hypothesis that simple statistics of a large, noisy, unlabelled corpus of natural language can be exploited to discover some of the structure which exists in natural language automatically. The strategy is to initially assume no knowledge of the structures present in natural language, save that they might be found by analysing statistical regularities which pertain between a word and the words which typically surround it in the corpus.To achieve this, various statistical methods are applied to define similarity between statistical distributions, and to infer a structure for a domain given knowledge of the similarities which pertain within it. Using these tools, it is shown that it is possible to form a hierarchical classification of many domains, including words in natural language. When this is done, it is shown that all the major syntactic categories can be obtained, and the classification is both relatively complete, and very much in accord with a standard linguistic conception of how words are classified in natural language.Once this has been done, the categorisation derived is used as the basis of a similar classification of short sequences of words. If these are analysed in a similar way, then several syntactic categories can be derived. These include simple noun phrases, various tensed forms of verbs, and simple prepositional phrases. Once this has been done, the same technique can be applied one level higher, and at this level simple sentences and verb phrases, as well as more complicated noun phrases and prepositional phrases, are shown to be derivable

Semi-Supervised Named Entity Recognition:\ud Learning to Recognize 100 Entity Types with Little Supervision\ud

Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as proper names, biological species, and temporal expressions. There has been growing interest in this field of research since the early 1990s. In this thesis, we document a trend moving away from handcrafted rules, and towards machine learning approaches. Still, recent machine learning approaches have a problem with annotated data availability, which is a serious shortcoming in building and maintaining large-scale NER systems. \ud \ud In this thesis, we present an NER system built with very little supervision. Human supervision is indeed limited to listing a few examples of each named entity (NE) type. First, we introduce a proof-of-concept semi-supervised system that can recognize four NE types. Then, we expand its capacities by improving key technologies, and we apply the system to an entire hierarchy comprised of 100 NE types. \ud \ud Our work makes the following contributions: the creation of a proof-of-concept semi-supervised NER system; the demonstration of an innovative noise filtering technique for generating NE lists; the validation of a strategy for learning disambiguation rules using automatically identified, unambiguous NEs; and finally, the development of an acronym detection algorithm, thus solving a rare but very difficult problem in alias resolution. \ud \ud We believe semi-supervised learning techniques are about to break new ground in the machine learning community. In this thesis, we show that limited supervision can build complete NER systems. On standard evaluation corpora, we report performances that compare to baseline supervised systems in the task of annotating NEs in texts. \u

A model for automated topic spotting in a mobile chat based mathematics tutoring environment

Systems of writing have existed for thousands of years. The history of civilisation and the history of writing are so intertwined that it is hard to separate the one from the other. These systems of writing, however, are not static. They change. One of the latest developments in systems of writing is short electronic messages such as seen on Twitter and in MXit. One novel application which uses these short electronic messages is the Dr Math® project. Dr Math is a mobile online tutoring system where pupils can use MXit on their cell phones and receive help with their mathematics homework from volunteer tutors around the world. These conversations between pupils and tutors are held in MXit lingo or MXit language – this cryptic, abbreviated system 0f ryting w1ch l0ks lyk dis. Project μ (pronounced mu and indicating MXit Understander) investigated how topics could be determined in MXit lingo and Project μ's research outputs spot mathematics topics in conversations between Dr Math tutors and pupils. Once the topics are determined, supporting documentation can be presented to the tutors to assist them in helping pupils with their mathematics homework. Project μ made the following contributions to new knowledge: a statistical and linguistic analysis of MXit lingo provides letter frequencies, word frequencies, message length statistics as well as linguistic bases for new spelling conventions seen in MXit based conversations; a post-stemmer for use with MXit lingo removes suffixes from the ends of words taking into account MXit spelling conventions allowing words such as equashun and equation to be reduced to the same root stem; a list of over ten thousand stop words for MXit lingo appropriate for the domain of mathematics; a misspelling corrector for MXit lingo which corrects words such as acount and equates it to account; and a model for spotting mathematical topics in MXit lingo. The model was instantiated and integrated into the Dr Math tutoring platform. Empirical evidence as to the effectiveness of the μ Topic Spotter and the other contributions is also presented. The empirical evidence includes specific statistical tests with MXit lingo, specific tests of the misspelling corrector, stemmer, and feedback mechanism, and an extensive exercise of content analysis with respect to mathematics topics

Placeable and localizable elements in translation memory systems

Translation memory systems (TM systems) are software packages used in computer-assisted translation (CAT) to support human translators. As an example of successful natural language processing (NLP), these applications have been discussed in monographic works, conferences, articles in specialized journals, newsletters, forums, mailing lists, etc. This thesis focuses on how TM systems deal with placeable and localizable elements, as defined in 2.1.1.1. Although these elements are mentioned in the cited sources, there is no systematic work discussing them. This thesis is aimed at filling this gap and at suggesting improvements that could be implemented in order to tackle current shortcomings. The thesis is divided into the following chapters. Chapter 1 is a general introduction to the field of TM technology. Chapter 2 presents the conducted research in detail. The chapters 3 to 12 each discuss a specific category of placeable and localizable elements. Finally, chapter 13 provides a conclusion summarizing the major findings of this research project

Learning Functional Prepositions

In first language acquisition, what does it mean for a grammatical category to have been acquired, and what are the mechanisms by which children learn functional categories in general? In the context of prepositions (Ps), if the lexical/functional divide cuts through the P category, as has been suggested in the theoretical literature, then constructivist accounts of language acquisition would predict that children develop adult-like competence with the more abstract units, functional Ps, at a slower rate compared to their acquisition of lexical Ps. Nativists instead assume that the features of functional P are made available by Universal Grammar (UG), and are mapped as quickly, if not faster, than the semantic features of their lexical counterparts. Conversely, if Ps are either all lexical or all functional, on both accounts of acquisition we should observe few differences in learning. Three empirical studies of the development of P were conducted via computer analysis of the English and Spanish sub-corpora of the CHILDES database. Study 1 analyzed errors in child usage of Ps, finding almost no errors in commission in either language, but that the English learners lag in their production of functional Ps relative to lexical Ps. That no such delay was found in the Spanish data suggests that the English pattern is not universal. Studies 2 and 3 applied novel measures of phrasal (P head + nominal complement) productivity to the data. Study 2 examined prepositional phrases (PPs) whose head-complement pairs appeared in both child and adult speech, while Study 3 considered PPs produced by children that never occurred in adult speech. In both studies the productivity of Ps for English children developed faster than that of lexical Ps. In Spanish there were few differences, suggesting that children had already mastered both orders of Ps early in acquisition. These empirical results suggest that at least in English P is indeed a split category, and that children acquire the syntax of the functional subset very quickly, committing almost no errors. The UG position is thus supported. Next, the dissertation investigates a \u27soft nativist\u27 acquisition strategy that composes the distributional analysis of input, minimal a priori knowledge of the possible co-occurrence of morphosyntactic features associated with functional elements, and linguistic knowledge that is presumably acquired via the experience of pragmatic, communicative situations. The output of the analysis consists in a mapping of morphemes to the feature bundles of nominative pronouns for English and Spanish, plus specific claims about the sort of knowledge required from experience. The acquisition model is then extended to adpositions, to examine what, if anything, distributional analysis can tell us about the functional sequences of PPs. The results confirm the theoretical position according to which spatiotemporal Ps are lexical in character, rooting their own extended projections, and that functional Ps express an aspectual sequence in the functional superstructure of the PP