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A growing elderly population has created a need for innovative eldercare technologies. The use of a home robot to assist with daily tasks is one such example. In this paper we describe an interface for human-robot interaction, which uses built-in speech recognition in Android phones to control a mobile robot. We discuss benefits of using a smartphone for speech-based robot control and present speech recognition accuracy results for younger and older adults obtained with an Android smartphone.

Psychiatric evaluation reports represent a rich and still mostly-untapped source of information for developing systems for automatic diagnosis and treatment of mental health problems. These reports contain free-text structured within sections using a convention of headings. We present a model for automatically detecting the position and type of different psychiatric evaluation report sections. We developed this model using a corpus of 150 sample reports that we gathered from the Web, and used sentences as a processing unit while section headings were used as labels of section type. From these labels we generated a unified hierarchy of labels of section types, and then learned n-gram models of the language found in each section. To model conventions for section order, we integrated these n-gram models with a Hierarchical Hidden Markov Model (HHMM) representing the probabilities of observed section orders found in the corpus, and then used this HHMM n-gram model in a decoding framework to infer the most likely section boundaries and section types for documents with their section labels removed. We evaluated our model over two tasks, namely, identifying section boundaries and identifying section types and orders. Our model significantly outperformed baselines for each task with an F 1 of 0.88 for identifying section types, and a 0.26 WindowDiff (W d) and 0.20 and (P k) scores, respectively, for identifying section boundaries.

Determining whether an event in a news article is a foreground or background event would be useful in many natural language processing tasks, for example, temporal relation extraction, summarization, or storyline generation. We introduce the task of distinguishing between foreground and background events in news articles as well as identifying the general temporal position of background events relative to the foreground period (past, present, future, and their combinations). We achieve good performance (0.73 F 1 for background vs. foreground and temporal position, and 0.79 F 1 for background vs. foreground only) on a dataset of news articles by leveraging discourse information in a featurized model. We release our implementation and annotated data for other researchers 1 .

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