Learn More
Automatic knowledge base population from text is an important technology for a broad range of approaches to learning by reading. Effective automated knowledge base population depends critically upon coreference resolution of entities across sources. Use of a wide range of features, both those that capture evidence for entity merging and those that argue(More)
Most recent unsupervised methods in vector space semantics for assessing thematic fit (e.g. create prototypical role-fillers without performing word sense disam-biguation. This leads to a kind of sparsity problem: candidate role-fillers for different senses of the verb end up being measured by the same " yardstick " , the single prototypical role-filler. In(More)
While several data sets for evaluating thematic fit of verb-role-filler triples exist, they do not control for verb polysemy. Thus, it is unclear how verb polysemy affects human ratings of thematic fit and how best to model that. We present a new dataset of human ratings on high vs. low-polysemy verbs matched for verb frequency , together with high vs.(More)
English. Thematic fit is the extent to which an entity fits a thematic role in the semantic frame of an event, e.g., how well humans would rate " knife " as an instrument of an event of cutting. We explore the use of the SENNA semantic role-labeller in defining a distributional space in order to build an unsupervised model of event-entity thematic fit(More)
A common problem in cognitive modelling is lack of access to accurate broad-coverage models of event-level surprisal. As shown in, e.g., Bicknell et al. (2010), event-level knowledge does affect human expectations for verbal arguments. For example, the model should be able to predict that mechanics are likely to check tires, while journalists are more(More)
Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents(More)
Grammatical structures for word-level sentiment detection. Title = {Grammatical structures for word-level sentiment detection}, } Links: • Data [ Abstract Existing work in fine-grained sentiment analysis focuses on sentences and phrases but ignores the contribution of individual words and their grammatical connections. This is because of a lack of both (1)(More)
We present an end-to-end pipeline including a user interface for the production of word-level annotations for an opinion-mining task in the information technology (IT) domain. Our pre-annotation pipeline selects candidate sentences for annotation using results from a small amount of trained annotation to bias the random selection over a large corpus. Our(More)
How can the relationships among information technology innovations be described and analyzed in a representative, dynamic, and scalable way? THEORETICAL FRAMEWORK Innovation concepts are interrelated in an idea network, where using an ecological perspective they can be likened to species in a competitive and symbiotic resource space. METHODS We apply(More)