<p class="MsoNormal">Este trabajo sienta las bases empíricas y metodológicas para la creación de una base de datos léxica que analice la morfología derivativa del inglés antiguo. Se revisa el estado de la cuestión, se formulan las hipótesis de partida, se proponen los principios descriptivos para el análisis de la derivación transparente y opaca y se avanza en la dirección explicativa al establecer los parámetros de análisis de las bases y de los adjuntos de las derivaciones que son relevantes para las explicaciones.</p> <p class="MsoNormal"><span style="mso-ansi-language: EN-US;" lang="EN-US">This article lays the empirical and methodological basis for the development of a lexical database that analyses the derivative morphology of Old English. After revising the state of the art; the working hypotheses are established and the descriptive principles for the analysis of tranparent as well as opaque derivation are established. Finally; a step is taken in the explanatory direction by defining the parameters of the analysis of the bases and adjunts of derivation that are relevant for explanation.</span></p>
This paper takes issue with the lexicon of Old English and, more specifically, with the existence of closing suffixes in word-formation. Closing suffixes are defined as base suffixes that prevent further suffixation by word-forming suffixes (Aronoff & Furhop 2002: 455). This is tantamount to saying that this is a study in recursivity, or the formation of derivatives from derived bases, as in anti-establish-ment, which requires the attachment of the prefix anti- to the derived input establishment.
The present analysis comprises all major lexical categories, that is, nouns, adjectives, verbs and adverbs and concentrates on suffixes because they represent the newest and the most productive process in Old English word-formation (Kastovsky 1992, 2006), as well as the set of morphemes that has survived into Present-day English without undergoing radical changes. Given this aim, the data retrieved from the lexical database of Old English Nerthus (www.nerthusproject.com) comprise 6,073 affixed (prefixed and suffixed) derivatives, including 3,008 nouns, 1,961 adjectives, 974 adverbs and 130 verbs. All of them have been analysed in order to isolate recursive formations.
The aim of this article is to measure the productivity of the Old English weak verb suffixes-ettan,-laecan,-sian,-nian,-lian,-erian and-cian from a synchronic point of view by taking into account the role played by hapax legomena. Productivity in the narrow sense P and global productivity P* are measured and frequency is calculated in terms of type and token. Three types of hapax legomena are distinguished, namely absolute hapaxes (unique formations that appear in one text), relative hapaxes (formations that appear in different texts, but only once in each text) and mixed hapaxes (a subsumption of both types). This typology of hapaxes puts the focus on-sian,-erian,-lian and-cian, which range between very low and zero productivity.
The grammatical description of Old English lacks complete and systematic lemmatization, which hinders Natural Language Processing studies in this language, as they strongly rely on the existence of large, annotated corpora. Moreover, the inflectional features of Old English preclude token-based automatic lemmatization. Therefore, specifically goal-oriented applications must be developed to account for the automatic lemmatization of specific variable categories. This article designs an automatic lemmatizer within the framework of Morphological Generation to address the type-based lemmatization of Old English class V strong verbs (L-Y). The lemmatizer is implemented with rules that account for inflectional, derivational and morphophonological variation. The generated forms are compared with the most relevant corpora of Old English for validation before being assigned a lemma. The lemmatizer is successful in supplying form-lemma associations not yet accounted for in the literature, and in identifying mismatches and areas for manual revision.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.