The language component of the FASTY predictive typing system

I describe the language component of FASTY, a text prediction system designed to improve text input efficiency for disabled users. The FASTY language component is based on state-of-the-art n-gram-based word-level and Part-of-Speech-level prediction and on a number of innovative modules (morphological analysis, collocation-based prediction, compound prediction) that are meant to enhance performance in languages other than English. Together with its modular architecture these novel techniques make it adaptable to a wide range of languages without sacrificing performance. Currently, versions for Dutch, German, French, Italian, and Swedish are supported. Going beyond the FASTY system, it will also be shown that the language component can be easily extended for the use with reduced keyboards, just by defining key-mapping tables, without needing to change the dictionary or the language model

The linguistics of gender

This chapter explores grammatical gender as a linguistic phenomenon. First, I define gender in terms of agreement, and look at the parts of speech that can take gender agreement. Because it relates to assumptions underlying much psycholinguistic gender research, I also examine the reasons why gender systems are thought to emerge, change, and disappear. Then, I describe the gender system of Dutch. The frequent confusion about the number of genders in Dutch will be resolved by looking at the history of the system, and the role of pronominal reference therein. In addition, I report on three lexical- statistical analyses of the distribution of genders in the language. After having dealt with Dutch, I look at whether the genders of Dutch and other languages are more or less randomly assigned, or whether there is some system to it. In contrast to what many people think, regularities do indeed exist. Native speakers could in principle exploit such regularities to compute rather than memorize gender, at least in part. Although this should be taken into account as a possibility, I will also argue that it is by no means a necessary implication

An automatic part-of-speech tagger for Middle Low German

Syntactically annotated corpora are highly important for enabling large-scale diachronic and diatopic language research. Such corpora have recently been developed for a variety of historical languages, or are still under development. One of those under development is the fully tagged and parsed Corpus of Historical Low German (CHLG), which is aimed at facilitating research into the highly under-researched diachronic syntax of Low German. The present paper reports on a crucial step in creating the corpus, viz. the creation of a part-of-speech tagger for Middle Low German (MLG). Having been transmitted in several non-standardised written varieties, MLG poses a challenge to standard POS taggers, which usually rely on normalized spelling. We outline the major issues faced in the creation of the tagger and present our solutions to them

The impact of morphological errors in phrase-based statistical machine translation from German and English into Swedish

We have investigated the potential for improvement in target language morphology when translating into Swedish from English and German, by measuring the errors made by a state of the art phrase-based statistical machine translation system. Our results show that there is indeed a performance gap to be filled by better modelling of inflectional morphology and compounding; and that the gap is not filled by simply feeding the translation system with more training data

A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

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