The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Zhiqing Shao - One of the best experts on this subject based on the ideXlab platform.
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A semantic processing model for Sentence understanding based on cognitive learning
2011 IEEE 3rd International Conference on Communication Software and Networks, 2011Co-Authors: Yan Li, Zhiqing ShaoAbstract:Sentence has a very prominent position in the research field of Natural Language Understanding. The task of Sentence understanding includes two stages, Sentence Parsing and semantic processing. Sentence Parsing resides in the fundamental level, while semantic understanding involves lexcial and higher discourse analysis. As Sentence understanding has compact connections with human cognition, this paper will introduce how cognitive models are integrated, with machine learning algorithms (or models), into the procedures of Sentence Parsing and semantic processing.
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Cognitive Learning for Sentence Understanding
New Advances in Machine Learning, 2010Co-Authors: Yi Guo, Zhiqing ShaoAbstract:In the research field of natural language understanding, Sentence stands a very prominent position in text processing. The process of Sentence understanding involves computing the meaning of a Sentence based on analysis of meanings of its individual words. Research procedures in Sentence understanding examine the representations and processes that connect the identification of individual words in text reading (Culter, 1995; Balota, 1994) with mapping Sentence meanings to relevant mental models (Johnson-Laird, 1983) or discourse representations (Kintsch, 1988; van Eijck & Kamp, 1997). The task of Sentence understanding includes two stages, Sentence Parsing and semantic processing. Sentence Parsing resides in the fundamental level, while semantic understanding involves lexcial and higher discourse analysis. Sentence understanding has compact connections with human cognition, thus this chaper will introduce how cognitive models are integrated, with machine learning algorithms (or models), into the procedures of Sentence Parsing and semantic processing.
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A cognitive interactionist Sentence parser with simple recurrent networks
Information Sciences, 2010Co-Authors: Yi Guo, Zhiqing Shao, Nan HuaAbstract:Sentence Parsing has a long history in the research fields of machine learning and natural language processing. The state-of-the-art technologies used to tackle this task include those based on statistical language learning. In the meantime, human Sentence Parsing has attracted massive research efforts for decades in the field of cognitive psychology. A range of behaviouristic experiments verify that the interactionist approach is a sensible and effective way to simulate the human Parsing mechanism. This paper proposes a novel and effective Sentence parser, the Cognitive Interactionist Parser (CIParser), which incorporates the cognitive interactionist approach with semantic information and simple recurrent networks to extend and enrich the technologies for Sentence Parsing. Considering the Parsing efficiency, CIParser processes the semantic information of nouns and verbs in current stage. The performance of the Cognitive Interactionist Parser is evaluated using elaborately designed experiments using the noted SUSANNE Corpus. The experimental results demonstrate that the Cognitive Interactionist Parser surpasses two state-of-the-art statistical parsers in two classical measures, Precision and Recall, of Information Retrieval (IR).
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The Cognitive Interactionist Approach of Sentence Parsing with Simple Recurrent Networks
2008 Second International Symposium on Intelligent Information Technology Application, 2008Co-Authors: Yi Guo, Zhiqing ShaoAbstract:Sentence Parsing has a long research history in the fields of machine learning and natural language processing. The state-of-the-art techniques for tackling this task are mostly based on statistical language learning. How human Parsing Sentences is also an important research topic attracting research efforts for decades in the field of cognitive psychology. Some behavioristic experiments have convinced that the interactionist approach is rational and effective to simulate human Parsing mechanism. This paper presents a Sentence parser, the Interactionist Parser, which incorporated the cognitive interactionist approach with semantic information and simple recurrent networks, to extend and enrich the techniques for Sentence Parsing. Thinking of the Parsing efficiency, the semantic information of two word types, noun and verb, are included during the Parsing procedure in current stage. The experimental results demonstrate that the Interactionist Parser has comparability with the state-of-the-art Parsing techniques based on statistical language learning.
Yi Guo - One of the best experts on this subject based on the ideXlab platform.
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Cognitive Learning for Sentence Understanding
New Advances in Machine Learning, 2010Co-Authors: Yi Guo, Zhiqing ShaoAbstract:In the research field of natural language understanding, Sentence stands a very prominent position in text processing. The process of Sentence understanding involves computing the meaning of a Sentence based on analysis of meanings of its individual words. Research procedures in Sentence understanding examine the representations and processes that connect the identification of individual words in text reading (Culter, 1995; Balota, 1994) with mapping Sentence meanings to relevant mental models (Johnson-Laird, 1983) or discourse representations (Kintsch, 1988; van Eijck & Kamp, 1997). The task of Sentence understanding includes two stages, Sentence Parsing and semantic processing. Sentence Parsing resides in the fundamental level, while semantic understanding involves lexcial and higher discourse analysis. Sentence understanding has compact connections with human cognition, thus this chaper will introduce how cognitive models are integrated, with machine learning algorithms (or models), into the procedures of Sentence Parsing and semantic processing.
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A cognitive interactionist Sentence parser with simple recurrent networks
Information Sciences, 2010Co-Authors: Yi Guo, Zhiqing Shao, Nan HuaAbstract:Sentence Parsing has a long history in the research fields of machine learning and natural language processing. The state-of-the-art technologies used to tackle this task include those based on statistical language learning. In the meantime, human Sentence Parsing has attracted massive research efforts for decades in the field of cognitive psychology. A range of behaviouristic experiments verify that the interactionist approach is a sensible and effective way to simulate the human Parsing mechanism. This paper proposes a novel and effective Sentence parser, the Cognitive Interactionist Parser (CIParser), which incorporates the cognitive interactionist approach with semantic information and simple recurrent networks to extend and enrich the technologies for Sentence Parsing. Considering the Parsing efficiency, CIParser processes the semantic information of nouns and verbs in current stage. The performance of the Cognitive Interactionist Parser is evaluated using elaborately designed experiments using the noted SUSANNE Corpus. The experimental results demonstrate that the Cognitive Interactionist Parser surpasses two state-of-the-art statistical parsers in two classical measures, Precision and Recall, of Information Retrieval (IR).
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The Cognitive Interactionist Approach of Sentence Parsing with Simple Recurrent Networks
2008 Second International Symposium on Intelligent Information Technology Application, 2008Co-Authors: Yi Guo, Zhiqing ShaoAbstract:Sentence Parsing has a long research history in the fields of machine learning and natural language processing. The state-of-the-art techniques for tackling this task are mostly based on statistical language learning. How human Parsing Sentences is also an important research topic attracting research efforts for decades in the field of cognitive psychology. Some behavioristic experiments have convinced that the interactionist approach is rational and effective to simulate human Parsing mechanism. This paper presents a Sentence parser, the Interactionist Parser, which incorporated the cognitive interactionist approach with semantic information and simple recurrent networks, to extend and enrich the techniques for Sentence Parsing. Thinking of the Parsing efficiency, the semantic information of two word types, noun and verb, are included during the Parsing procedure in current stage. The experimental results demonstrate that the Interactionist Parser has comparability with the state-of-the-art Parsing techniques based on statistical language learning.
Nan Hua - One of the best experts on this subject based on the ideXlab platform.
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A cognitive interactionist Sentence parser with simple recurrent networks
Information Sciences, 2010Co-Authors: Yi Guo, Zhiqing Shao, Nan HuaAbstract:Sentence Parsing has a long history in the research fields of machine learning and natural language processing. The state-of-the-art technologies used to tackle this task include those based on statistical language learning. In the meantime, human Sentence Parsing has attracted massive research efforts for decades in the field of cognitive psychology. A range of behaviouristic experiments verify that the interactionist approach is a sensible and effective way to simulate the human Parsing mechanism. This paper proposes a novel and effective Sentence parser, the Cognitive Interactionist Parser (CIParser), which incorporates the cognitive interactionist approach with semantic information and simple recurrent networks to extend and enrich the technologies for Sentence Parsing. Considering the Parsing efficiency, CIParser processes the semantic information of nouns and verbs in current stage. The performance of the Cognitive Interactionist Parser is evaluated using elaborately designed experiments using the noted SUSANNE Corpus. The experimental results demonstrate that the Cognitive Interactionist Parser surpasses two state-of-the-art statistical parsers in two classical measures, Precision and Recall, of Information Retrieval (IR).
Jiebo Luo - One of the best experts on this subject based on the ideXlab platform.
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robust visual textual sentiment analysis when attention meets tree structured recursive neural networks
ACM Multimedia, 2016Co-Authors: Quanzeng You, Liangliang Cao, Hailin Jin, Jiebo LuoAbstract:Sentiment analysis is crucial for extracting social signals from social media content. Due to huge variation in social media, the performance of sentiment classifiers using single modality (visual or textual) still lags behind satisfaction. In this paper, we propose a new framework that integrates textual and visual information for robust sentiment analysis. Different from previous work, we believe visual and textual information should be treated jointly in a structural fashion. Our system first builds a semantic tree structure based on Sentence Parsing, aimed at aligning textual words and image regions for accurate analysis. Next, our system learns a robust joint visual-textual semantic representation by incorporating 1) an attention mechanism with LSTM (long short term memory) and 2) an auxiliary semantic learning task. Extensive experimental results on several known data sets show that our method outperforms existing the state-of-the-art joint models in sentiment analysis. We also investigate different tree-structured LSTM (T-LSTM) variants and analyze the effect of the attention mechanism in order to provide deeper insight on how the attention mechanism helps the learning of the joint visual-textual sentiment classifier.
Donald Mitchell - One of the best experts on this subject based on the ideXlab platform.
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Modifier Attachment in Sentence Parsing: Evidence from Dutch
The Quarterly Journal of Experimental Psychology Section A, 1996Co-Authors: Marc Brysbaert, Donald MitchellAbstract:Current theories of Parsing suggest a wide variety of mechanisms by which modifiers, such as relative clauses, may be related to constituents that offer more than one potential attachment site. Some, like the tuning hypothesis, are based on the premise that people's Parsing performance is shaped by prior exposure to language. Others (e.g. garden-path theory and construal theory) play down any potential role of past linguistic experience, stressing instead the varying influences of structural characteristics of the Sentence in question. The two views encourage differing expectations about cross-linguistic variation in Parsing preference. A questionnaire study and two on-line experiments were carried out to investigate attachment preferences in Dutch. The results pose a number of problems for the majority of the existing Parsing models and are clearly inconsistent with some of the traditional theories. In contrast, the findings are compatible with models incorporating Parsing mechanisms that are tuned by la...
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Effects of context in human Sentence Parsing: Evidence against a discourse-based proposal mechanism.
Journal of Experimental Psychology: Learning Memory and Cognition, 1992Co-Authors: Donald Mitchell, Martin Corley, Alan GarnhamAbstract:Two subject-paced reading experiments were carried out to examine the way in which discourse information exerts its influence in Sentence comprehension
