Experiment for Using Web Information to do Query and Document Expansion
نویسندگان
چکیده
ImageCLEF photo task of this year is a little different from those of previous years. The caption field in image annotations and the narrative field in the text queries are removed, and the visual queries (example images) are also removed from the image collection too. In the new definition, the information that can be employed for queries and images is less than before, so that it becomes harder to match query words and annotations directly. To deal with this issue, we explore the web to expand queries and documents. Many images and text information can be found in the web, but we should face the noise embedded. The experiment shows the query expansion improves performance about 16.11%. The document expansion brings too much noise and the performance decrease 28.24% after expansion. The media mapping method that we proposed in previous years is used for query expansion too. The results of formal runs show this method is still very useful in the new task definitions. ACM
منابع مشابه
Query Architecture Expansion in Web Using Fuzzy Multi Domain Ontology
Due to the increasing web, there are many challenges to establish a general framework for data mining and retrieving structured data from the Web. Creating an ontology is a step towards solving this problem. The ontology raises the main entity and the concept of any data in data mining. In this paper, we tried to propose a method for applying the "meaning" of the search system, But the problem ...
متن کاملQuery expansion based on relevance feedback and latent semantic analysis
Web search engines are one of the most popular tools on the Internet which are widely-used by expert and novice users. Constructing an adequate query which represents the best specification of users’ information need to the search engine is an important concern of web users. Query expansion is a way to reduce this concern and increase user satisfaction. In this paper, a new method of query expa...
متن کاملRRLUFF: Ranking function based on Reinforcement Learning using User Feedback and Web Document Features
Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...
متن کاملروش جدید متنکاوی برای استخراج اطلاعات زمینه کاربر بهمنظور بهبود رتبهبندی نتایج موتور جستجو
Today, the importance of text processing and its usages is well known among researchers and students. The amount of textual, documental materials increase day by day. So we need useful ways to save them and retrieve information from these materials. For example, search engines such as Google, Yahoo, Bing and etc. need to read so many web documents and retrieve the most similar ones to the user ...
متن کاملTowards Supporting Exploratory Search over the Arabic Web Content: The Case of ArabXplore
Due to the huge amount of data published on the Web, the Web search process has become more difficult, and it is sometimes hard to get the expected results, especially when the users are less certain about their information needs. Several efforts have been proposed to support exploratory search on the web by using query expansion, faceted search, or supplementary information extracted from exte...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007