Minimum Description Length Models for Unsupervised Learning of Morphology

This thesis work introduces an approach to unsupervised learning of morphological structure of human languages. We focus on morphologically rich languages and the goal is to construct a knowledge-free and language-independent model. This model works by receiving a long list of words in a language and is expected to learn how to segment the input words in a way that the resulting segments correspond to morphemes in the target language. Several improvements inspired by well-motivated linguistic principles of morphology of languages are introduced to the proposed MDL-based learning algorithm. In addition to the learning algorithm, a new evaluation method and corresponding resources are introduced. Evaluation of morphological segmentations is a challenging task due to the inherent ambiguity of natural languages and underlying morphological processes such as fusion which encumber identification of unique 'correct' segmentations for words. Our evaluation method addresses the problem of segmentation evaluation with a focus on consistency of segmentations. Our approach is tested on data from Finnish, Turkish, and Russian. Evaluation shows a gain in performance over the state of the art

Improving the Timeliness, Accuracy, and Completeness of Mortality Reporting Using FHIR Apps and Machine Learning

There are approximately 56 million deaths per year world-wide, with millions happening in the United States. Accurate and timely mortality reporting is essential for gathering this important public health data in order to formulate emergency response to epidemics and new disease threats, to prevent communicable diseases such as flu, and to determine vital statistics such as life expectancy, mortality trends, etc. However, accurate collection and aggregation of high-quality mortality data remains an ongoing challenge due to issues such as the average low frequency with which physicians perform death certification, inconsistent training in determining the causes of death, complex data flow between the funeral home, the certifying physician and the registrar, and non-standard practices of data acquisition and transmission. We propose a smart application for medical providers at the point-of-care which will use \glsfirst{fhir} to integrate directly with the medical record, provide the practitioner with context for the death, and use machine learning techniques to enable the reporting of an accurate and complete causal chain of events leading to the death.Ph.D

Painolliset äärellistilaiset menetelmät oikaisulukuun

This dissertation is a large-scale study of spell-checking and correction using finite-state technology. Finite-state spell-checking is a key method for handling morphologically complex languages in a computationally efficient manner. This dissertation discusses the technological and practical considerations that are required for finite-state spell-checkers to be at the same level as state-of-the-art non-finite-state spell-checkers. Three aspects of spell-checking are considered in the thesis: modelling of correctly written words and word-forms with finite-state language models, applying statistical information to finite-state language models with a specific focus on morphologically complex languages, and modelling misspellings and typing errors using finite-state automata-based error models. The usability of finite-state spell-checkers as a viable alternative to traditional non-finite-state solutions is demonstrated in a large-scale evaluation of spell-checking speed and the quality using languages with morphologically different natures. The selected languages display a full range of typological complexity, from isolating English to polysynthetic Greenlandic with agglutinative Finnish and the Saami languages somewhere in between.Tässä väitöskirjassa tutkin äärellistilaisten menetelmien käyttöä oikaisuluvussa. Äärellistilaiset menetelmät mahdollistavat sananmuodostukseltaan monimutkaisempien kielten, kuten suomen tai grönlannin, sanaston sujuvan käsittelyn oikaisulukusovelluksissa. Käsittelen tutkielmassani tieteellisiä ja käytännöllisiä toteutuksia, jotka ovat tarpeen, jotta tällaisia sananmuodostukseltaan monimutkallisempia kieliä voisi käsitellä oikaisuluvussa yhtä tehokkaasti kuin yksinkertaisempia kieliä, kuten englantia tai muita indo-eurooppalaisia kieliä nyt käsitellään. Tutkielmassa esitellään kolme keskeistä tutkimusongelmaa, jotka koskevat oikaisuluvun toteuttamista sanarakenteeltaan monimutkaisemmille kielille: miten mallintaa oikeinkirjoitetut sanamuodot äärellistilaisin mallein, miten soveltaa tilastollista mallinnusta monimutkaisiin sanarakenteisiin kuten yhdyssanoihin, ja miten mallintaa kirjoitusvirheitä äärellistilaisin mentelmin. Tutkielman tuloksena esitän äärellistilaisia oikaisulukumenetelmiä soveltuvana vaihtoehtona nykyisille oikaisulukimille, tämän todisteena esitän mittaustuloksia, jotka näyttävät, että käyttämäni menetelmät toimivat niin rakenteellisesti yksinkertaisille kielille kuten englannille yhtä hyvin kuin nykyiset menetelmät että rakenteellisesti monimutkaisemmille kielille kuten suomelle, saamelle ja jopa grönlannille riittävän hyvin tullakseen käytetyksi tyypillisissä oikaisulukimissa