Learning to Measure Quality of Queries for Automatic Query Suggestion

Our approach to query suggestion – generating can- didate query terms from the set of retrieved docu- ments – is related to pseudo-relevance feedback [3], a method for query

Finally, being able to measure the energy consumption of a single query is important, as recent approaches are taking into direct account the search engine power consumption to

According to our observation on the topics in INEX 2012 SBS Track, we find that there are some queries that are different from others, we call them the Type2 queries

In the offline phase, we first generate a large pool of candidate synonyms for query tokens using various techniques leveraging user behav- ioral data, inventory and

Given an original query q = {w q1 , ..., w qn }, the process of expanding q is three- fold: (1) retrieving relevant tweets answering q using a retrieval model, (2) se- lecting a set

Finally, the ranking function learned by the learning algorithm in the training phase have been used to weight and rank the terms of the 2016 topics.. The top-10 ranked terms of

Figure 3, 4, 5 shows the architecture of our system. The preprocessing module includes stop-word filtering and stemming. We use an open source search engine, Lucene, as our

Since such atoms are not given as input, we address the problem of (e ffi ciently) computing such restricting atoms within an approximate query answering algorithm after showing