Learning Semantic Categories from Search Clickthrough Logs Using Laplacian Label Propagation
نویسندگان
چکیده
منابع مشابه
Learning Semantic Categories from Clickthrough Logs
As the web grows larger, knowledge acquisition from the web has gained increasing attention. In this paper, we propose using web search clickthrough logs to learn semantic categories. Experimental results show that the proposed method greatly outperforms previous work using only web search query logs.
متن کاملKnowledge Base Population using Semantic Label Propagation
A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision (DS), which is a method to const...
متن کاملSemantic Label Sharing for Learning with Many Categories
In an object recognition scenario with tens of thousands of categories, even a small number of labels per category leads to a very large number of total labels required. We propose a simple method of label sharing between semantically similar categories. We leverage the WordNet hierarchy to define semantic distance between any two categories and use this semantic distance to share labels. Our a...
متن کاملLearning semantic models from event logs
In this work we present an approach to extract the business logic of an application based on its generated logs. To do so we use process mining techniques to extract the model from the logs of an industrial application which are often large and hence difficult to analyze. We propose here a methodology to group the log according to similar elements, so that it can be presented to the user in sep...
متن کاملMinimally Invasive Randomization fro Collecting Unbiased Preferences from Clickthrough Logs
Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well known that the probability of a user clicking on a result is strongly biased toward documents presented higher in the result set irrespective of relevance. We introduce a simple method to modify the presentation of searc...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transactions of the Japanese Society for Artificial Intelligence
سال: 2010
ISSN: 1346-0714,1346-8030
DOI: 10.1527/tjsai.25.196