One of the challenges to information extraction is the requirement of human annotated examples. Current successful approaches alleviate this problem by employing some form of distant supervision i.e., look into knowledge bases such as Freebase as a source of supervision to create more examples. While this is perfectly reasonable, most distant supervision methods rely on a hand coded background ...

Most of existing work learn sentiment-specific word representation for improving Twitter sentiment classification, which encoded both n-gram and distant supervised tweet sentiment information in learning process. They assume all words within a tweet have the same sentiment polarity as the whole tweet, which ignores the word its own sentiment polarity. To address this problem, we propose to lear...

In this paper we present a semantic tagger aiming to detect relevant entities in French medical documents and tagging them with their appropriate semantic class. These experiments has been carried out in the framework of CLEF2015 eHealth contest that proposes a tagset of ten classes from UMLS taxonomy. The system presented uses a set of binary classifiers, and a combination mechanisms for combi...

Microblogging websites such as Twitter offer a wealth of insight into a population’s current mood. Automated approaches to identify general sentiment toward a particular topic often perform two steps: Topic Identification and Sentiment Analysis. Topic Identification first identifies tweets that are relevant to a desired topic (e.g., a politician or event), and Sentiment Analysis extracts each t...

Most relation extraction methods, especially in the domain of biology, rely on machine learning methods to classify a cooccurring pair of entities in a sentence to be related or not. Such an approach requires a training corpus, which involves expert annotation and is tedious, timeconsuming, and expensive. We overcome this problem by the use of existing knowledge in structured databases to autom...

The application of machine learning to quantum information processing has recently attracted keen interest, particularly for the optimization of control parameters in quantum tasks without any pre-programmed knowledge. By adapting the machine learning technique, we present a novel protocol in which an arbitrarily initialized device at a learner’s location is taught by a provider located at a di...

Although IP multicast has been rapidly evolving in the last few years, the current Internet is not fully multicast-enabled because of the lack of robust inter-domain routing and controlling protocols. In this paper, we present an alternative architecture for data distribution on Internet called PSRM that realizes semantic reliable, loose synchronized, multicast data delivery. PSRM is not based ...

Human listeners can keep track of statistical regularities among temporally adjacent elements in both speech and musical streams. However, for speech streams, when statistical regularities occur among nonadjacent elements, only certain types of patterns are acquired. Here, using musical tone sequences, the authors investigate nonadjacent learning. When the elements were all similar in pitch ran...