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So far, there have been few descriptions on creating structures capable of storing lexicographic data, ISO 24613:2008 being one of the latest. Another one is by Spohr (2012), who designs a multifunctional lexical resource which is able to store data of different types of dictionaries in a user-oriented way. Technically, his design is based on the principle of a hierarchical XML/OWL (eXtensible Markup Language/Web Ontology Language) representation model. This article follows another route in describing a model based on entities and relations between them; MySQL (usually referred to as: Structured Query Language) describes a database system of tables containing data and definitions of relations between them. The model was developed in the context of the project "Scientific eLexicography for Africa" and the lexicographic database to be built thereof will be implemented with MySQL. The principles of the ISO model and of Spohr's model are adhered to with one major difference in the implementation strategy: we do not place the lemma in the centre of attention, but the sense description — all other elements, including the lemma, depend on the sense description. This article also describes the contained lexicographic data sets and how they have been collected from different sources. As our aim is to compile several prototypical internet dictionaries (a monolingual Northern Sotho dictionary, a bilingual learners' Xhosa–English dictionary and a bilingual Zulu–English dictionary), we describe the necessary microstructural elements for each of them and which principles we adhere to when designing different ways of accessing them. We plan to make the model and the (empty) database with all graphical user interfaces that have been developed, freely available by mid-2015.
This article describes an English Zulu learners’ dictionary that is part of a larger set of information tools, namely an online Zulu course, an e-dictionary of possessives (which was implemented earlier) accompanied by training software offering translation tasks on several levels, and an ontology of morphemic items categorizing and describing all parts of speech of Zulu. The underlying lexicographic database contains the usual type of lexicographic data, such as translation equivalents and their respective morphosyntactic data, but its entries have been extended with data related to the lessons of the online course in order to enable the learner to link both tools autonomously. The ‘outer matter’ is integrated into the website in the form of several texts on additional web pages (how-to-use, typical outputs, grammar tables, information on morphosyntactic rules, etc.). The dictionary comprises a modular system, where each module fulfils one of the necessary functions.
So far, Sepedi negations have been considered more from the point of view of lexicographical treatment. Theoretical works on Sepedi have been used for this purpose, setting as an objective a neat description of these negations in a (paper) dictionary. This paper is from a different perspective: instead of theoretical works, corpus linguistic methods are used: (1) a Sepedi corpus is examined on the basis of existing descriptions of the occurrences of a relevant verb, looking at its negated forms from a purely prescriptive point of view; (2) a "corpus-driven" strategy is employed, looking only for sequences of negation particles (or morphemes) in order to list occurring constructions, without taking into account the verbs occurring in them, apart from their endings. The approach in (2) is only intended to show a possible methodology to extend existing theories on occurring negations. We would also like to try to help lexicographers to establish a frequency-based order of entries of possible negation forms in their dictionaries by showing them the number of respective occurrences. As with all corpus linguistic work, however, we must regard corpus evidence not as representative, but as tendencies of language use that can be detected and described. This is especially true for Sepedi, for which only few and small corpora exist. This paper also describes the resources and tools used to create the necessary corpus and also how it was annotated with part of speech and lemmas. Exploring the quality of available Sepedi part-of-speech taggers concerning verbs, negation morphemes and subject concords may be a positive side result.
Between classical symbolic word sense disambiguation (wsd) using explicit deep semantic representations of sentences and texts and statistical wsd using word co-occurrence information, there is a recent tendency towards mediating methods. Similar to so-called lightweight semantics (Marek, 2009) we suggest to only make sparse use of semantic information. We describe an approximation model based upon flat underspecified discourse representation structures (FUDRSs, cf. Eberle, 2004) that weighs knowledge about context structure, lexical semantic restrictions and interpretation preferences. We give a catalogue of guidelines for human annotation of texts by corresponding indicators. Using this, the reliability of an analysis tool that implements the model can be tested with respect to annotation precision and disambiguation prediction and how both can be improved by bootstrapping the knowledge of the system using corpus information. For the balanced test corpus considered the recognition rate of the preferred reading is 80-90% (depending on the smoothing of parse errors).
Electronic dictionaries should support dictionary users by giving them guidance in text production and text reception, alongside a user-definable offer of lexicographic data for cognitive purposes. In this article, we sketch the principles of an interactive and dynamic electronic dictionary aimed at text production and text reception guiding users in innovative ways, especially with respect to difficult, complicated or confusing issues. The lexicographer has to do a very careful analysis of the nature of the possible problems to suggest an optimal solution for a specific problem. We are of the opinion that there are numerous complex situations where users need more detailed support than currently available in e-dictionaries, enabling them to make valid and correct choices. For highly complex situations, we suggest guidance through a decision tree-like device. We assume that the solutions proposed here are not specific to one language only but can, after careful analysis, be applied to e-dictionaries in different languages across the world.
In a previous article (Faaß et al., 2012), a first attempt was made at documenting and encoding morphemic units of two South African Bantu languages, i.e. Northern Sotho and Zulu, with the aim of describing and storing the morphemic units of these two languages in a single relational database, structured as a hierarchical ontology. As a follow-up, the current article describes the implementation of our part-of-speech ontology. We give a detailed description of the morphemes and categories contained in the database, highlighting the need and reasons for a flexible ontology which will provide for both language specific and general linguistic information. By giving a detailed account of the methodology for the population of the database, we provide linguists from other Bantu languages with a road map for extending the database to also include their languages of specialization.
In der Computerlinguistik ist eine kaskadische Prozessierung von Texten üblich. Dabei werden diese zuerst segmentiert (tokenisiert), d.h. Tokens und ggf. Satzgrenzen werden erkannt. Dabei entsteht meist eine Liste bzw. eine einspaltige Tabelle, die sukzessive durch weitere Prozessierungschritte um zusätzliche Spalten – also positionale Annotationen wie z.B. Wortarten und Lemmata für die Tokens in der ersten Spalte – ergänzt wird. Bei der Tokenisierung werden alle Spatien (Leerzeichen) gelöscht. Schon immer problematisch waren dabei Interpunktionszeichen, da diese äußerst ambig sein können, aber auch mehrteilige Namen, die Leerzeichen enthalten und eigentlich zusammengehören. Dieser Beitrag fokussiert auf den Apostroph, der in vielfältiger Weise in den Texten Udo Lindenbergs eingesetzt wird sowie auf mehrteilige Namen, die wir als Tokens erhalten möchten. Wir nutzen dafür das komplette Lindenberg-Archiv des song-korpus.de-Repositoriums, kategorisieren die auftretenden Phänomene, erstellen einen Goldstandard und entwickeln ein teils regel-, teils auf maschinellem Lernen basierendes Segmentierungswerkzeug, das insbesondere die auftretenden Apostrophe, aber auch -lexikonbasiert - mehrteilige Namen nach unseren Vorstellungen erkennt und tokenisiert. Im Anschluss trainieren wir den RNN-Tagger (Schmid, 2019) und zeigen auf, dass ein spezifisch für diese Texte angepasstes Training zu Genauigkeiten ≥ 96% führt. Dabei entsteht nicht nur ein Goldstandard des annotierten Korpus, das dem Songkorpus-Repositorium zur Verfügung gestellt wird, sondern auch eine angepasste Version des RNN-Taggers (verfügbar auf github), die für ähnliche Texte verwendet werden kann.
Towards a part-of-speech ontology: encoding morphemic units of two South African Bantu languages
(2012)
This article describes the design of an electronic knowledge base, namely a morpho-syntactic database structured as an ontology of linguistic categories, containing linguistic units of two related languages of the South African Bantu group: Northern Sotho and Zulu. These languages differ significantly in their surface orthographies, but are very similar on the lexical and sub-lexical levels. It is therefore our goal to describe the morphemes of these languages in a single common database in order to outline and interpret commonalities and differences in more detail. Moreover, the relational database which is developed defines the underlying morphemic units (morphs) for both languages. It will be shown that the electronic part-of-speech ontology goes hand in hand with part-of-speech tagsets that label morphemic units. This database is designed as part of a forthcoming system providing lexicographic and linguistic knowledge on the official South African Bantu languages.