Deception detection in dialogues

In the social media era, it is commonplace to engage in written conversations. People sometimes even form connections across large distances, in writing. However, human communication is in large part non-verbal. This means it is now easier for people to hide their harmful intentions. At the same time, people can now get in touch with more people than ever before. This puts vulnerable groups at higher risk for malevolent interactions, such as bullying, trolling, or predatory behavior. Furthermore, such growing behaviors have most recently led to waves of fake news and a growing industry of deceit creators and deceit detectors. There is now an urgent need for both theory that explains deception and applications that automatically detect deception. In this thesis I address this need with a novel application that learns from examples and detects deception reliably in natural-language dialogues. I formally define the problem of deception detection and identify several domains where it is useful. I introduce and evaluate new psycholinguistic features of deception in written dialogues for two datasets. My results shed light on the connection between language, deception, and perception. They also underline the challenges and difficulty of assessing perceptions from written text. To automatically learn to detect deception I first introduce an expressive logical model and then present a probabilistic model that simplifies the first and is learnable from labeled examples. I introduce a belief-over-belief formalization, based on Kripke semantics and situation calculus. I use an observation model to describe how utterances are produced from the nested beliefs and intentions. This allows me to easily make inferences about these beliefs and intentions given utterances, without needing to explicitly represent perlocutions. The agents’ belief states are filtered with the observed utterances, resulting in an updated Kripke structure. I then translate my formalization to a practical system that can learn from a small dataset and is able to perform well using very little structural background knowledge in the form of a relational dynamic Bayesian network structure

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Lexical segmentation and word recognition in fluent aphasia

The current thesis reports a psycholinguistic study of lexical segmentation and word recognition in fluent aphasia.When listening to normal running speech we must identify individual words from a continuous stream before we can extract a linguistic message from it. Normal listeners are able to resolve the segmentation problem without any noticeable difficulty. In this thesis I consider how fluent aphasic listeners perform the process of lexical segmentation and whether any of their impaired comprehension of spoken language has its provenance in the failure to segment speech normally.The investigation was composed of a series of 5 experiments which examined the processing of both explicit acoustic and prosodic cues to word juncture and features which affect listeners' segmentation of the speech stream implicitly, through inter-lexical competition of potential word matchesThe data collected show that lexical segmentation of continuous speech is compromised in fluent aphasia. Word hypotheses do not always accrue appropriate activational information from all of the available sources within the time frame in which segmentation problem is normally resolved. The fluent aphasic performance, although quantitatively impaired compared to normal, reflects an underlying normal competence; their processing seldom displays a totally qualitatively different processing profile to normal. They are able to engage frequency, morphological structure, and imageability as modulators of activation. Word class, a feature found to be influential in the normal resolution of segmentation is not used by the fluent aphasic studied. In those cases of occasional failure to adequately resolve segmentation by automatic frequency mediated activation, fluent aphasics invoke the metalinguistic influence of real world plausibility of alternative parses