Deep learning for extracting protein-protein interactions from biomedical literature

State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.Comment: Accepted for publication in Proceedings of the 2017 Workshop on Biomedical Natural Language Processing, 10 pages, 2 figures, 6 table

A Review on Human-Computer Interaction and Intelligent Robots

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research

A Multi-Task Approach to Incremental Dialogue State Tracking

Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality phenomenon in our model’s performance by analyzing how early our models can produce correct predictions and how stable those predictions are. We find that the Multi-Task Learning-based model achieves state-of-the-art results for incremental processing

From Algorithmic to Neural Beamforming

Human interaction increasingly relies on telecommunication as an addition to or replacement for immediate contact. The direct interaction with smart devices, beyond the use of classical input devices such as the keyboard, has become common practice. Remote participation in conferences, sporting events, or concerts is more common than ever, and with current global restrictions on in-person contact, this has become an inevitable part of many people's reality. The work presented here aims at improving these encounters by enhancing the auditory experience. Augmenting fidelity and intelligibility can increase the perceived quality and enjoyability of such actions and potentially raise acceptance for modern forms of remote experiences. Two approaches to automatic source localization and multichannel signal enhancement are investigated for applications ranging from small conferences to large arenas. Three first-order microphones of fixed relative position and orientation are used to create a compact, reactive tracking and beamforming algorithm, capable of producing pristine audio signals in small and mid-sized acoustic environments. With inaudible beam steering and a highly linear frequency response, this system aims at providing an alternative to manually operated shotgun microphones or sets of individual spot microphones, applicable in broadcast, live events, and teleconferencing or for human-computer interaction. The array design and choice of capsules are discussed, as well as the challenges of preventing coloration for moving signals. The developed algorithm, based on Energy-Based Source Localization, is discussed and the performance is analyzed. Objective results on synthesized audio, as well as on real recordings, are presented. Results of multiple listening tests are presented and real-time considerations are highlighted. Multiple microphones with unknown spatial distribution are combined to create a large-aperture array using an end-to-end Deep-Learning approach. This method combines state-of-the-art single-channel signal separation networks with adaptive, domain-specific channel alignment. The Neural Beamformer is capable of learning to extract detailed spatial relations of channels with respect to a learned signal type, such as speech, and to apply appropriate corrections in order to align the signals. This creates an adaptive beamformer for microphones spaced on the order of up to 100m. The developed modules are analyzed in detail and multiple configurations are considered for different use cases. Signal processing inside the Neural Network is interpreted and objective results are presented on simulated and semi-simulated datasets