Paraphrase generation is widely used for various natural language processing (NLP) applications such as question answering, multi-document summarization, and machine translation. In this study, we identify the problem...
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ISBN:
(纸本)9786165518871
Paraphrase generation is widely used for various natural language processing (NLP) applications such as question answering, multi-document summarization, and machine translation. In this study, we identify the problems occurring in the process of applying existing probabilistic model-based methods to agglutinative languages, and provide solutions by reflecting the inherent characteristics of agglutinative languages. More specifically, we propose and evaluate a sentential paraphrase generation (SPG) method for the Korean language using Support Vector Machines (SVM) with a string kernel. The quality of generated paraphrases is evaluated using three criteria: (1) meaning preservation, (2) grammaticality, and (3) equivalence. Our experiment shows that the proposed method outperformed a probabilistic model-based method by 12%, 16%, and 17%, respectively, with respect to the three criteria. Copyright 2014 by Hancheol Park, Gahgene Gweon, Ho-Jin Choi, Jeong Heo, and Pum-Mo Ryu.
Query expansion adds related words to a user query in order to improve retrieval results. It's an important step in information retrieval. Most of current query expansion methods pay attention to specific expansio...
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Query expansion adds related words to a user query in order to improve retrieval results. It's an important step in information retrieval. Most of current query expansion methods pay attention to specific expansion strategies or algorithms, while neglecting the query itself. In reaction to the phenomenon, a multistrategy query expansion method based on semantics was proposed. This method started by analyzing the semantic structure of user query, and adopted corresponding strategy to select expansion terms. The expansion words are derived from three parts: WordNet, massive web page set and search engine performance evaluation data, which were merged semantically in each expansion algorithm later. The experiment showed this method can improve retrieval results to some extent.
DBSCAN is one of the most common density-based clustering algorithms. While multiple works tried to present an appropriate estimate for needed parameters we propose an alternating optimization algorithm, which finds a...
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ISBN:
(纸本)9781479975617
DBSCAN is one of the most common density-based clustering algorithms. While multiple works tried to present an appropriate estimate for needed parameters we propose an alternating optimization algorithm, which finds a locally optimal parameter combination. The algorithm is based on the combination of two hierarchical versions of DBSCAN, which can be generated by fixing one parameter and iterating through possible values of the second parameter. Due to monotonicity of the neighborhood sets and the core-condition, successive levels of the hierarchy can efficiently be computed. An local optimal parameter combination can be determined using internal cluster validation measures. In this work we are comparing the measures edge-correlation and silhouette coefficient. For the latter we propose a density-based interpretation and show a respective computational efficient estimate to detect non-convex clusters produced by DBSCAN. Our results show, that the algorithm can automatically detect a good DBSCAN clustering on a variety of cluster scenarios.
Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made...
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Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery - a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.
This paper introduces the RASH Framework, i.e., a set of specifications and tools for writing academic articles in RASH, a simplified version of HTML. RASH focuses strictly on writing the content of the paper leaving ...
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This paper introduces the RASH Framework, i.e., a set of specifications and tools for writing academic articles in RASH, a simplified version of HTML. RASH focuses strictly on writing the content of the paper leaving all the issues about its validation, visualisation, conversion, and data extraction to the tools developed within the framework.
This paper suggests a new interactive system based on visualization of the user's knowledge schema to aid the process of information search and knowledge discovery. The system, inspired from the human memory model...
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Monte Carlo Tree Search (MCTS) has become a widely popular sampled-based search algorithm for two-player games with perfect information. When actions are chosen simultaneously, players may need to mix between their st...
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Online search in games has always been a core interest of artif icial intel1igencc Advances made in search for perfect inform ation games (such as Chess. Checkers, Go. and Backgamm on) have led to Al capable of defeat...
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We introduce a Horn description logic called Horn-DL, which is strictly and essentially richer than Horn-RegI, Horn-SHIQ and Horn-SROIQ, while still has PTime data complexity. In comparison with Horn-SROIQ, Horn-DL ad...
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