Suchergebnisse

The promise of the Semantic Web is that future web pages will be annotated not only with bright colors and fancy fonts as they are now, but with annotation extracted from large domain ontologies that specify, to a computer in a way that it can exploit, what information is contained on the given web page. The presence of this information will allow software agents to examine pages and to make decisions about content as humans are able to do now. The classic method of building an ontology is to gather a committee of experts in the domain to be modeled by the ontology, and to have this committee.

Humans have a natural tendency to form social groups, and individual behaviours are thought to be strongly influenced by a salient sense of belonging to one or more such groups. It can be expected, therefore, that there will be behaviours that are specific to the group(s) to which a person currently feels they are interacting with and that some of these behaviours will manifest in topics and patterns of linguistic style associated with those groups. Here we explore this idea by attempting to identify group specific patterns of language usage in social media data from Twitter and Reddit. Topic models are used to infer patterns of language usage and group structures are either provided with the data (Reddit) inferred from the follower network (Twitter). We apply a Bayesian graphical model to infer community-topic associations, finding that substantially more coherent associations can often be identified than with a naive probability-based approach. Strong associations are found between groups and topics with both approaches, indicating that the methods used to (independently) identify groups and topics represent real underlying patterns of social communication and promising fruitful investigation of human social behaviour using these or similar techniques.

Metaphor is one of the most important elements of human communication, especially in informal settings such as social media. There have been a number of datasets created for metaphor identification, however, this task has proven difficult due to the nebulous nature of metaphoricity. In this paper, we present a crowd-sourcing approach for the creation of a dataset for metaphor identification, that is able to rapidly achieve large coverage over the different usages of metaphor in a given corpus while maintaining high accuracy. We validate this methodology by creating a set of 2,500 manually annotated tweets in English, for which we achieve inter-annotator agreement scores over 0.8, which is higher than other reported results that did not limit the task. This methodology is based on the use of an existing classifier for metaphor in order to assist in the identification and the selection of the examples for annotation, in a way that reduces the cognitive load for annotators and enables quick and accurate annotation. We selected a corpus of both general language tweets and political tweets relating to Brexit and we compare the resulting corpus on these two domains. As a result of this work, we have published the first dataset of tweets annotated for metaphors, which we believe will be invaluable for the development, training and evaluation of approaches for metaphor identification in tweets.

The Monnet project offers a solution to this cross-language information access problem by using a novel combination of Machine Translation and Semantic Web Technology. Monnet business users will be able to have transparent access to company information across national and linguistic boundaries. Monnet public services users will find answers to their questions independent of the country's official languages. A key solution to this problem is to deal with the information at a semantic level. Monnet achieves this through semantically aware term translation based on a novel approach that integrates ontology-based domain semantics with linguistic information from the domain lexicon. The Monnet solution will change the way business and government provide, exchange and integrate information, driving global standardization efforts.