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Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval-2015 Task 3 on Answer Selection in cQA, which included two subtasks: (a) classifying answers as(More)
One long-standing challenge in robotics is the realization of mobile autonomous robots able to operate safely in existing human workplaces in a way that their presence is accepted by the human occupants. We describe the development of a multi-ton robotic forklift intended to operate alongside human personnel, handling palletized materials within existing,(More)
We describe a multimodal framework for interacting with an autonomous robotic forklift. A key element enabling effective interaction is a wireless, handheld tablet with which a human supervisor can command the forklift using speech and sketch. Most current sketch interfaces treat the canvas as a blank slate. In contrast, our interface uses live and(More)
This paper extends upon our previous work using i-vectors for speaker diarization. We examine the effectiveness of spectral clustering as an alternative to our previous approach using K-means clustering and adapt a previously-used heuristic to estimate the number of speakers. Additionally, we consider an iterative optimization scheme and experiment with its(More)
This paper describes the SemEval–2016 Task 3 on Community Question Answering , which we offered in English and Ara-bic. For English, we had three sub-tasks: Question–Comment Similarity (subtask A), Question–Question Similarity (B), and Question–External Comment Similarity (C). For Arabic, we had another subtask: Rerank the correct answers for a new question(More)
People with motor disabilities often face substantial challenges using interfaces designed for manual interaction. Although such obstacles might be partially alleviated by automatic speech recognition, these individuals may also have co-occurring speech-language challenges that result in high recognition error rates. In this paper, we investigate how(More)
Recent studies show that Gaussian mixture model (GMM) weights carry less, yet complimentary, information to GMM means for language and dialect recognition. However, state-of-the-art language recognition systems usually do not use this information. In this research, a non-negative factor analysis (NFA) approach is developed for GMM weight decomposition and(More)
publicly paper and electronic copies of this thesis document in whole and in part in any medium now known or hereafter created. Abstract Vector representations for language have been shown to be useful in a number of Natural Language Processing (NLP) tasks. In this thesis, we aim to investigate the effectiveness of word vector representations for the(More)
Community question answering platforms need to automatically rank answers and questions with respect to a given question. In this paper, we present the approaches for the Answer Selection and Question Retrieval tasks of SemEval-2016 (task 3). We develop a bag-of-vectors approach with various vector-and text-based features, and different neu-ral network(More)