Refine
Document Type
- Part of a Book (5)
- Article (2)
- Working Paper (1)
Has Fulltext
- yes (8)
Keywords
- Automatische Sprachanalyse (8) (remove)
Publicationstate
- Veröffentlichungsversion (3)
- Postprint (2)
Reviewstate
- (Verlags)-Lektorat (5)
- Peer-Review (1)
Publisher
Formalisierung von Kontext und sprachlichem Wissen mit Prioritisierter Circumscription (VM-Memo 55)
(1994)
Integrated Linguistic Annotation Models and Their Application in the Domain of Antecedent Detection
(2011)
Seamless integration of various, often heterogeneous linguistic resources in terms of their output formats and a combined analysis of the respective annotation layers are crucial tasks for linguistic research. After a decade of concentration on the development of formats to structure single annotations for specific linguistic issues, in the last years a variety of specifications to store multiple annotations over the same primary data has been developed. The paper focuses on the integration of the knowledge resource logical document structure information into a text document to enhance the task of automatic anaphora resolution both for the task of candidate detection and antecedent selection. The paper investigates data structures necessary for knowledge integration and retrieval.
This article introduces the topic of ‘‘Multilingual language resources and interoperability’’. We start with a taxonomy and parameters for classifying language resources. Later we provide examples and issues of interoperatability, and resource architectures to solve such issues. Finally we discuss aspects of linguistic formalisms and interoperability.
Communication across all language barriers has long been a goal of humankind. In recent years, new technologies have enabled this at least partially. New approaches and different methods in the field of Machine Translation (MT) are continuously being improved, modified, and combined, as well. Significant progress has already been achieved in this area; many automatic translation tools, such as Google Translate and Babelfish, can translate not only short texts, but also complete web pages in real time. In recent years, new advances are being made in the mobile area; Googles Translate app for Android and iOS, for example, can recognize and translate words within photographs taken by the mobile device (to translate a restaurant menu, for instance). Despite this progress, a “perfect” machine translation system seems to be an impossibility because a machine translation system, however advanced, will always have some limitations. Human languages contain many irregularities and exceptions, and consequently go through a constant process of change, which is difficult to measure or to be processed automatically. This paper gives a short introduction of the state of the art of MT. It examines the following aspects: types of MT, the most conventional and widely developed approaches, and also the advantages and disadvantages of these different paradigms.
Natural language Processing tools are mostly developed for and optimized on newspaper texts, and often Show a substantial performance drop when applied to other types of texts such as Twitter feeds, Chat data or Internet forum posts. We explore a range of easy-to-implement methods of adapting existing part-of-speech taggers to improve their performance on Internet texts. Our results show that these methods can improve tagger performance substantially.