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Information and communication technology are the important support technology to effective energy distribution and consumption. With the national power reform and electricity sale opened, the new requirements and goals for user demand side management and response were put forward. The efficient mining and application of consumption data should be the important basis of demand side management and response...
At present, the user experience provided by smart mobile terminals is limited to the battery capacity. This paper focuses on how to improve the energy efficiency of terminals in OFDMA-based wireless multicast systems with frequency-selective channels. We assume that multicast terminals can switch to sleep mode during the transmission of some OFDM symbols according to their OFDMA frame-level quality...
Smart Grid requires lots of applications in the terminals to sense the environment or control the intelligent devices. Due to the low cost and high function, wireless sensors have been deployed in power grid wildly. Depending on wireless sensor network (WSN), the grid can build a two-way communication system, and then customers can interact extensively with the network, both in providing power consumption...
in this paper, a novel approach was proposed for recognizing resident user type in the power consumption field, based ME some interesting features had been discussed. The probabilistic feature functions are used instead of binary feature functions, it is one of the several differences between this model and the most of the previous ME based model. We also explore several novel features in our model,...
With the development of natural language processing (NLP) technology, the need for automatic named entity recognition (NER) is highlighted in order to enhance the performance of information extraction systems. In this paper, a hybrid model for Chinese person based on conditional random fields model is proposed, which fuses multiple features. It differentiates from most of the previous approaches,...
Entity relation extraction is the important research field in many languages processing, such as the knowledge management, semantic network, information retrieval and information extraction. In this paper, some specific entity relations can be extracted in the football match domain, firstly, the definition of the five types relation had been defined. Based on the conditional random fields, aiming...
This paper proposed a hybrid method of the Chinese location recognition which combines conditional random fields model and pattern-selection. The conditional random fields model is used based on statistic method, some interesting features have been proposed, the new probabilistic feature is proposed, which are used instead of binary feature functions, however, it is one of the several differences...
A novel approach of the entity relation extraction is proposed by this paper, it is different from the previous approaches, and the syntactic knowledge extraction is specific section, which automatically extracts the characteristic words and patterns based on hierarchy bootstrapping machine learning. It advocates using a small amount of seed information and a large collection of easily-obtained unlabeled...
Entity relation extraction (RE) is one of the important research fields in information extraction, we regard RE as a classification problem in this paper. This paper presents a novel approach, conditional random fields (CRFs)-based machine learning is used to extract entity relation between entities from Chinese texts, ten features have been designed for entity relation extraction, which includes...
This paper proposes a new approach for personal name recognition in Chinese language domain. Combining rule-based and statistical method, we consider wonderful linguistics knowledge; firstly step, we collect personal name as candidate entity, and send it into statistical model to decide whether it is the relevant entity, the conditional random fields (CRFs) is used in this paper. At the same time,...
Person, location and organization have been always mentioned as a bottleneck of a named entity recognition (NER) system. Automatic recognition of Chinese organization name is the most difficult problem in NER tasks. This paper presents a new approach of Chinese organization name recognition based on cascaded conditional random fields. In the proposed approach, we first recognize the person name and...
Entity relation extraction (RE) is an very important research domain in information extraction, we can regard RE as a classification problem in this paper, RE is still original study field in Chinese language now, maximum entropy (ME)-based machine learning is the first time to be used to extract entity relations between named entities from Chinese texts, Thirteen features have been designed for entity...
In the task of Chinese word segmentation, there are two main segmentation ambiguities, overlapping ambiguity and combination ambiguity. The paper analyzes properties of ambiguities and supposes multi-knowledge approach to disambiguate. Multi-knowledge refers to the knowledge from statistic of large corpus and syntactic, semantic or discourse information about ambiguous words. Class based N-gram and...
The paper presents hierarchy bootstrapping as an alternative approach to learning from a large quantity of unlabeled data in the Chinese language domain. It advocates using a small amount of seed information and a large collection of easily-obtained unlabeled data. Hierarchy bootstrapping initializes a learner with seed information; then it iterates applying the learner to calculate for the unlabeled...
A specific prototype information service system was proposed by this paper, which can send interesting information to user with database search way from unstructured text. In order to achieve this goal, two fundamental issues were studied by using maximum entropy (ME) algorithm, which is named entity recognition and relation extraction. Our named entity recognition approach is distinguished from most...
In Chinese word segmentation task, combination ambiguity is one of challenges not being well settled. The main obstacle exists in the detection of ambiguous words in given texts and their proper segmentations. This paper puts forward a practical approach to automatically collecting ambiguous words and disambiguating based on maximum entropy principle. The experimental result reveals the approach of...
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