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

Optimization of the Morpher Morphology Engine Using Knowledge Base Reduction Techniques

Morpher is a novel morphological rule induction engine designed and developed for agglutinative languages. The Morpher engine models inflection using general string-based transformation rules and it can learn multiple arbitrary affix types, too. In order to scale the engine to training sets containing millions of examples, we need an efficient management of the generated rule base. In this paper we investigate and present several optimization techniques using rule elimination based on context length, support and cardinality parameters. The performed evaluation tests show that using the proposed optimization techniques, we can reduce the average inflection time to 0.52 %, the average lemmatization time to 2.59 % and the number of rules to 2.25 % of the original values, while retaining a high correctness ratio of 98 %. The optimized model can execute inflection and lemmatization in acceptable time after training millions of items, unlike other existing methods like Morfessor, MORSEL or MorphoChain

PLPrepare: A Grammar Checker for Challenging Cases

This study investigates one of the Polish language’s most arbitrary cases: the genitive masculine inanimate singular. It collects and ranks several guidelines to help language learners discern its proper usage and also introduces a framework to provide detailed feedback regarding arbitrary cases. The study tests this framework by implementing and evaluating a hybrid grammar checker called PLPrepare. PLPrepare performs similarly to other grammar checkers and is able to detect genitive case usages and provide feedback based on a number of error classifications

Disambiguoiva morfologinen jäsennys probabilistisilla sekvenssimalleilla

A morphological tagger is a computer program that provides complete morphological descriptions of sentences. Morphological taggers find applications in many NLP fields. For example, they can be used as a pre-processing step for syntactic parsers, in information retrieval and machine translation. The task of morphological tagging is closely related to POS tagging but morphological taggers provide more fine-grained morphological information than POS taggers. Therefore, they are often applied to morphologically complex languages, which extensively utilize inflection, derivation and compounding for encoding structural and semantic information. This thesis presents work on data-driven morphological tagging for Finnish and other morphologically complex languages. There exists a very limited amount of previous work on data-driven morphological tagging for Finnish because of the lack of freely available manually prepared morphologically tagged corpora. The work presented in this thesis is made possible by the recently published Finnish dependency treebanks FinnTreeBank and Turku Dependency Treebank. Additionally, the Finnish open-source morphological analyzer OMorFi is extensively utilized in the experiments presented in the thesis. The thesis presents methods for improving tagging accuracy, estimation speed and tagging speed in presence of large structured morphological label sets that are typical for morphologically complex languages. More specifically, it presents a novel formulation of generative morphological taggers using weighted finite-state machines and applies finite-state taggers to context sensitive spelling correction of Finnish. The thesis also explores discriminative morphological tagging. It presents structured sub-label dependencies that can be used for improving tagging accuracy. Additionally, the thesis presents a cascaded variant of the averaged perceptron tagger. In presence of large label sets, a cascaded design results in substantial reduction of estimation speed compared to a standard perceptron tagger. Moreover, the thesis explores pruning strategies for perceptron taggers. Finally, the thesis presents the FinnPos toolkit for morphological tagging. FinnPos is an open-source state-of-the-art averaged perceptron tagger implemented by the author.Disambiguoiva morfologinen jäsennin on ohjelma, joka tuottaa yksikäsitteisiä morfologisia kuvauksia virkkeen sanoille. Tällaisia jäsentimiä voidaan hyödyntää monilla kielenkäsittelyn osa-alueilla, esimerkiksi syntaktisen jäsentimen tai konekäännösjärjestelmän esikäsittelyvaiheena. Kieliteknologisena tehtävänä disambiguoiva morfologinen jäsennys muistuttaa perinteistä sanaluokkajäsennystä, mutta se tuottaa hienojakoisempaa morfologista informaatiota kuin perinteinen sanaluokkajäsennin. Tämän takia disambiguoivia morfologisia jäsentimiä hyödynnetäänkin pääsääntöisesti morfologisesti monimutkaisten kielten, kuten suomen kielen, kieliteknologiassa. Tällaisissa kielissä käytetään paljon sananmuodostuskeinoja kuten taivutusta, johtamista ja yhdyssananmuodostusta. Väitöskirjan esittelemä tutkimus liittyy morfologisesti rikkaiden kielten disambiguoivaan morfologiseen jäsentämiseen koneoppimismenetelmin. Vaikka suomen disambiguoivaa morfologista jäsentämistä on tutkittu aiemmin (esim. Constraint Grammar -formalismin avulla), koneoppimismenetelmiä ei ole aiemmin juurikaan sovellettu. Tämä johtuu siitä että jäsentimen oppimiseen tarvittavia korkealuokkaisia morfologisesti annotoituja korpuksia ei ole ollut avoimesti saatavilla. Tässä väitöskirjassa esitelty tutkimus hyödyntää vastikään julkaistuja suomen kielen dependenssijäsennettyjä FinnTreeBank ja Turku Dependency Treebank korpuksia. Lisäksi tutkimus hyödyntää suomen kielen avointa morfologista OMorFi-jäsennintä. Väitöskirja esittelee menetelmiä jäsennystarkkuuden parantamiseen ja jäsentimen opetusnopeuden sekä jäsennysnopeuden kasvattamiseen. Väitöskirja esittää uuden tavan rakentaa generatiivisia jäsentimiä hyödyntäen painollisia äärellistilaisia koneita ja soveltaa tällaisia jäsentimiä suomen kielen kontekstisensitiiviseen oikeinkirjoituksentarkistukseen. Lisäksi väitöskirja käsittelee diskriminatiivisia jäsennysmalleja. Se esittelee tapoja hyödyntää morfologisten analyysien osia jäsennystarkkuuden parantamiseen. Lisäksi se esittää kaskadimallin, jonka avulla jäsentimen opetusaika lyhenee huomattavasi. Väitöskirja esittää myös tapoja jäsenninmallien pienentämiseen. Lopuksi esitellään FinnPos, joka on kirjoittaman toteuttama avoimen lähdekoodin työkalu disambiguoivien morfologisten jäsentimien opettamiseen

Towards a machine-learning architecture for lexical functional grammar parsing

Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages. The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing. In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages

The Family Name as Socio-Cultural Feature and Genetic Metaphor: From Concepts to Methods

A recent workshop entitled The Family Name as Socio-Cultural Feature and Genetic Metaphor: From Concepts to Methods was held in Paris in December 2010, sponsored by the French National Centre for Scientific Research (CNRS) and by the journal Human Biology. This workshop was intended to foster a debate on questions related to the family names and to compare different multidisciplinary approaches involving geneticists, historians, geographers, sociologists and social anthropologists. This collective paper presents a collection of selected communications

Tokenization with Factorized Subword Encoding

In recent years, language models have become increasingly larger and more complex. However, the input representations for these models continue to rely on simple and greedy subword tokenization methods. In this paper, we propose a novel tokenization method that factorizes subwords onto discrete triplets using a VQ-VAE model. The effectiveness of the proposed tokenization method, referred to as the Factorizer, is evaluated on language modeling and morpho-syntactic tasks for 7 diverse languages. Results indicate that this method is more appropriate and robust for morphological tasks than the commonly used byte-pair encoding (BPE) tokenization algorithm.Comment: Findings of ACL 202