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We present a method that learns word embedding for Twitter sentiment classification in this paper. Most existing algorithms for learning continuous word representations typically only model the syntactic context of words but ignore the sentiment of text. This is problematic for sentiment analysis as they usually map words with similar syntactic context but(More)
Sensor networks are long-running computer systems with many sensing/compute nodes working to gather information about their environment, process and fuse that information, and in some cases, actuate control mechanisms in response. Like traditional parallel systems, communication between nodes is of fundamental importance, but is typically accomplished via(More)
Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a unified, bottom-up fashion. The model first learns sentence representation with convolutional neural(More)
This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate nearest neighbor approach called Locality Sensitive Hashing(More)
LTP (Language Technology Platform) is an integrated Chinese processing platform which includes a suite of high performance natural language processing (NLP) modules and relevant corpora. Especially for the syntactic and semantic parsing modules, we achieved good results in some relevant evaluations, such as CoNLL and SemEval. Based on XML internal data(More)
ZebraNet is a mobile, wireless sensor network in which nodes move throughout an environment working to gather and process information about their surroundings[10]. As in many sensor or wireless systems, nodes have critical resource constraints such as processing speed, memory size, and energy supply; they also face special hardware issues such as sensing(More)
Recent work has shown success in using continuous word embeddings learned from unlabeled data as features to improve supervised NLP systems, which is regarded as a simple semi-supervised learning mechanism. However, fundamental problems on effectively incorporating the word embedding features within the framework of linear models remain. In this study, we(More)
Semantic hierarchy construction aims to build structures of concepts linked by hypernym–hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of such relations. This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic(More)
Fanconi anemia (FA) is caused by mutations in 13 Fanc genes and renders cells hypersensitive to DNA interstrand cross-linking (ICL) agents. A central event in the FA pathway is mono-ubiquitylation of the FANCI-FANCD2 (ID) protein complex. Here, we characterize a previously unrecognized nuclease, Fanconi anemia-associated nuclease 1 (FAN1), that promotes ICL(More)