Hilke Reckman

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Mining data from online games provides a potential alternative to programming behavior and dialogue for characters in interactive narratives by hand. Human annotation of course-grained tasks can provide explanations that make the data more useful to an AI system, however human labor is expensive. We describe a semiautomatic methodology for recognizing tasks(More)
For SemEval-2013 Task 2, A and B (Sentiment Analysis in Twitter), we use a rulebased pattern matching system that is based on an existing ‘Domain Independent’ sentiment taxonomy for English, essentially a highly phrasal sentiment lexicon. We have made some modifications to our set of rules, based on what we found in the annotated training data that was made(More)
We discuss the use of data from a virtual world game for automated learning of words and grammatical constructions and their meanings. The language data is an integral part of the social interaction in the game and consists of chat dialogue, which is only constrained by the cultural context, as set by the nature of the provided virtual environment. This(More)
We use data from a virtual world game for automated learning of words and grammatical constructions and their meanings. The language data are an integral part of the social interaction in the game and consist of chat dialogue, which is only constrained by the cultural context, as set by the nature of the provided virtual environment. Building on previous(More)
Mining data from online games provides a potential alternative to programming behavior and dialogue for characters in interactive narratives by hand. Human annotation of course-grained tasks can provide explanations that make the data more useful to an AI system, however human labor is expensive. We describe a semi-automatic methodology for recognizing(More)
Machine learning and statistical methods have yielded impressive results in a wide variety of natural language processing tasks. These advances have generally been regarded as engineering achievements. In fact it is possible to argue that the success of machine learning methods is significant for our understanding of the cognitive basis of language(More)
The Delilah parser and generator for Dutch delivers three related levels of logical form: Stored Logical Form, Normal Logical Form and Flat Logical Form. SLF is produced by graph unification of attribute-value matrices as the value of a semantic feature. It is an underspecified level of semantic representation, as it does not spell out the exact scope of(More)
Verbs or adjectives and their nominalizations and certain adverb adjective pairs can be argued to introduce the same concept. This can be shown through inference patterns, which can be explained if we assume Davidsonian eventualities underlying all predicates. We make a contribution to the underlying state discussion by investigating the advantages and(More)
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