Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other seman...
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Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.
Currently a wide range of different adaptive and intelligent system solutions are being proposed for use in self-managing or autonomic networks. However, there are few means by which such proposals can be compared. Th...
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Sorting with stacks is a collection of problems that deal with sorting a sequence of numbers by pushing and popping the numbers to and from a given set of stacks. Multiple concrete decision or optimization questions a...
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Automatic identification of cardiac abnormalities through the ECG with a reduced lead system (less than the standard 12-lead) can provide a valuable easy to use and lower cost diagnostic alternative to ordinary 12-lea...
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This paper describes the SimCon (Simulated Context) Generator which combines data on the state of a Virtual Reality building with the SimCon Model to generate interactive location context for the rapid evaluation of S...
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Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model,by employing word clustering and syntactic parsing ***, In order...
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Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model,by employing word clustering and syntactic parsing ***, In order to overcome the defects of the classical HMM, Markov family model(MFM), a new statistical model was introduced. Secondly, to solve the problem of data sparseness, we propose a bottom-to-up hierarchical word clustering algorithm. Then we combine syntactic parsing with part-of-speech tagging. The Part-ofSpeech tagging experiments show that the improved PartOf-Speech tagging model has higher performance than Hidden Markov models(HMMs) under the same testing conditions, the precision is enhanced from 94.642% to97.235%.
Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-topeer knowledge management systems. In such a system, it is necessary to obtai...
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Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-topeer knowledge management systems. In such a system, it is necessary to obtain brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic peer-to-peer scenario.
Recently, several research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which have been targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, i...
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Recently, several research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which have been targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary for participants to provide brief descriptions of themselves, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peer a given query should be forwarded. In this talk, I propose the use of graph clustering techniques on knowledge bases for that purpose. After a brief round-trip over an ontology-based P2P knowledge management scenario, I will demonstrate the automatic generation of self-descriptions of peers' knowledge bases through the use of graph clustering. Viewing the knowledge base of a peer as a graph consisting of concepts and instances, one can employ clustering techniques to partition it into clusters of similar entities. From each cluster, the centroid can then be selected as a representative. This yields a list of entities giving an aggregated self description of the peer.
This paper presents our experiments on continuous audiovisual speech recognition. A number of bimodal systems using feature fusion or fusion within Hidden Markov Models are implemented. Experiments with different fusi...
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This paper presents our experiments on continuous audiovisual speech recognition. A number of bimodal systems using feature fusion or fusion within Hidden Markov Models are implemented. Experiments with different fusion techniques and their results are presented. Further the performance levels of the bimodal system and a unimodal speech recognizer under noisy conditions are compared.
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