Exploration of Query Difficult in Information Retrieval Project Abstract:
Search engines provide responses to user queries by retrieving a ranked list of documents that are relevant to the query. Retrieval is achieved by judging the similarity of the documents in the collection with the query.
Many information retrieval (IR) systems vary in performance. Systems that perform well on average by retrieving relevant document can have poor results for some queries. It is desirable that IR systems are able to identify “difficult” queries so that they can be handled properly: by asking the user to rephrase the query, by enabling the search engine to use a different retrieval method, or by providing feedback to the system administrator on the shortcomings of the document collection.
Exploration of Query Difficult in Information Retrieval project will explore two approaches to solving the problem of query difficulty: learning the indicators for difficult queries and building a model that captures the main components of a topic and the relationship between those components and topic difficulty. It will also consider practical applications that rely on this understanding, such as query expansion, distributed IR, missing documents and site find ability.