Novelty detection is to retrieve new information and filter redundancy fromgiven sentences that are relevant to a specific topic. In TREC2003, the authors tried an approach tonovelty detection with semantic distance c...Novelty detection is to retrieve new information and filter redundancy fromgiven sentences that are relevant to a specific topic. In TREC2003, the authors tried an approach tonovelty detection with semantic distance computation. The motivation is to expand a sentence byintroducing semantic information. Computation on semantic distance between sentences incorporatesWordNet with statistical information. The novelty detection is treated as a binary classificationproblem: new sentence or not. The feature vector, used in the vector space model for classification,consists of various factors, including the semantic distance from the sentence to the topic and thedistance from the sentence to the previous relevant context occurring before it. New sentences arethen detected with Winnow and support vector machine classifiers, respectively. Several experimentsare conducted to survey the relationship between different factors and performance. It is provedthat semantic computation is promising in novelty detection. The ratio of new sentence size torelevant size is further studied given different relevant document sizes. It is found that the ratioreduced with a certain speed (about 0.86). Then another group of experiments is performedsupervised with the ratio. It is demonstrated that the ratio is helpful to improve the noveltydetection performance.展开更多
It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semanti...It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semantic relations, which is formalized as ＂recursive directed graph＂. We focus on Chinese special sentence patterns, including the complex noun phrase, verb-complement structure, pivotal sentences, serial verb sentence and subject-predicate predicate sentence. Feature structure facilitates a richer Chinese semantic information extraction when compared with dependency structure. The results show that using recursive directed graph is more suitable for extracting Chinese complex semantic relations.展开更多
文摘Novelty detection is to retrieve new information and filter redundancy fromgiven sentences that are relevant to a specific topic. In TREC2003, the authors tried an approach tonovelty detection with semantic distance computation. The motivation is to expand a sentence byintroducing semantic information. Computation on semantic distance between sentences incorporatesWordNet with statistical information. The novelty detection is treated as a binary classificationproblem: new sentence or not. The feature vector, used in the vector space model for classification,consists of various factors, including the semantic distance from the sentence to the topic and thedistance from the sentence to the previous relevant context occurring before it. New sentences arethen detected with Winnow and support vector machine classifiers, respectively. Several experimentsare conducted to survey the relationship between different factors and performance. It is provedthat semantic computation is promising in novelty detection. The ratio of new sentence size torelevant size is further studied given different relevant document sizes. It is found that the ratioreduced with a certain speed (about 0.86). Then another group of experiments is performedsupervised with the ratio. It is demonstrated that the ratio is helpful to improve the noveltydetection performance.
基金Supported by the National Natural Science Foundation of China(61202193,61202304)the Major Projects of Chinese National Social Science Foundation(11&ZD189)+2 种基金the Chinese Postdoctoral Science Foundation(2013M540593,2014T70722)the Accomplishments of Listed Subjects in Hubei Prime Subject Developmentthe Open Foundation of Shandong Key Lab of Language Resource Development and Application
文摘It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semantic relations, which is formalized as ＂recursive directed graph＂. We focus on Chinese special sentence patterns, including the complex noun phrase, verb-complement structure, pivotal sentences, serial verb sentence and subject-predicate predicate sentence. Feature structure facilitates a richer Chinese semantic information extraction when compared with dependency structure. The results show that using recursive directed graph is more suitable for extracting Chinese complex semantic relations.
