A Survey on Automatically Mining Facets for Web Queries

In this paper, a detailed survey on different facet mining techniques, their advantages and disadvantages is carried out. Facets are any word or phrase which summarize an important aspect about the web query. Researchers proposed different efficient techniques which improves the user’s web query search experiences magnificently. Users are happy when they find the relevant information to their query in the top results. The objectives of their research are: (1) To present automated solution to derive the query facets by analyzing the text query; (2) To create taxonomy of query refinement strategies for efficient results; and (3) To personalize search according to user interest

A Review on Extracting Facets For Queries From Search Results

The delinquent of discovery query facets which are manifold groups of words or phrases that elucidate and abridge the satisfied covered by a query. The imperative facets of a query are frequently accessible and recurring in the query’s top regained documents in the style of lists, and query facets can be quarried out by collecting these momentous lists. a regular resolution, which we raise to as QDMiner, to robotically mine query facets by mining and federation common lists from free text, HTML tags, and recurrence regions within top search results. Experimental grades show that a bulky number of lists do happen and valuable query facets can be excavated by QDMiner

A New Search Recommendation for automatically Mining Query Facets

The delinquent of conclusion query facets which are numerous groups of words or phrases that explains and abridge the satisfied enclosed by a query. We accept that the imperative characteristics of a query are habitually existing and recurring in the query’s top regained documents in the style of lists, and question facets can be extracted out by collecting these significant lists. We advise a systematic solution, which we discuss to as QD Miner, to inevitably mine query facets by mining and grouping regular lists from free text, HTML tags, and reappearance regions within top search results. Experimental results appearance that a big number of lists do occur and useful query facets can be mined by QD Miner

Translating queries into snippets for improved query expansion

User logs of search engines have recently been applied successfully to improve various aspects of web search quality. In this paper, we will apply pairs of user queries and snippets of clicked results to train a machine translation model to bridge the “lexical gap ” between query and document space. We show that the combination of a query-to-snippet translation model with a large n-gram language model trained on queries achieves improved contextual query expansion compared to a system based on term correlations.