图 书 馆
从Web中测量隐含语义关系的相似性Measuring the Similarity between Implicit Semantic Relations from the Web |
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| 课程网址: | http://videolectures.net/www09_bollegala_mtsisr/ |
| 主讲教师: | Danushka Bollegala; Yutaka Matsuo; Mitsuru Ishizuka |
| 开课单位: | 东京大学 |
| 开课时间: | 2009-05-20 |
| 课程语种: | 日语 |
| 中文简介: |  |
| 课程简介: | Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as relation extraction, information retrieval and analogy detection. For example, consider the case in which a person knows a pair of entities (e.g. Google, YouTube), between which a particular relation holds (e.g. acquisition). The person is interested in retrieving other_such pairs with similar relations (e.g. Microsoft, Powerset). Existing keyword-based search engines cannot be applied directly in this case because, in keyword-based search, the goal is to retrieve documents that are relevant to the words used in a query -- not necessarily to the relations implied by a pair of words. We propose a relational similarity measure, using a Web search engine, to compute the similarity between semantic relations implied by two pairs of words. Our method has three components: representing the various semantic relations that exist between a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different patterns that express a particular semantic relation, and measuring the similarity between semantic relations using a metric learning approach. We evaluate the proposed method in two tasks: classifying semantic relations between named entities, and solving word-analogy questions. The proposed method outperforms all baselines in a relation classification task with a statistically significant average precision score of 0.74. Moreover, it reduces the time take by Latent Relational Analysis to process 374 word-analogy questions from 9 days to less than 6 hours, with a SAT score of 51%. |
| 关 键 词: | Web挖掘; 语义网; 计算机科学; 机器学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-29：lxf |
| 阅读次数: | 60 |