By Donald Metzler
Commercial internet se's akin to Google, Yahoo, and Bing are used on a daily basis by way of thousands of individuals around the globe. With their ever-growing refinement and utilization, it has turn into more and more tricky for educational researchers to take care of with the gathering sizes and different serious study matters with regards to internet seek, which has created a divide among the knowledge retrieval learn being performed inside of academia and undefined. Such huge collections pose a brand new set of demanding situations for info retrieval researchers.
In this paintings, Metzler describes powerful details retrieval versions for either smaller, classical information units, and bigger net collections. In a shift clear of heuristic, hand-tuned rating services and complicated probabilistic versions, he provides feature-based retrieval versions. The Markov random box version he information is going past the normal but ill-suited bag of phrases assumption in methods. First, the version can simply take advantage of a number of different types of dependencies that exist among question phrases, putting off the time period independence assumption that frequently accompanies bag of phrases versions. moment, arbitrary textual or non-textual beneficial properties can be utilized in the version. As he indicates, combining time period dependencies and arbitrary gains ends up in a truly strong, robust retrieval version. additionally, he describes numerous extensions, akin to an automated characteristic choice set of rules and a question enlargement framework. The ensuing version and extensions offer a versatile framework for powerful retrieval throughout quite a lot of initiatives and knowledge sets.
A Feature-Centric View of knowledge Retrieval offers graduate scholars, in addition to educational and business researchers within the fields of knowledge retrieval and internet seek with a latest standpoint on details retrieval modeling and net searches.
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Additional info for A Feature-Centric View of Information Retrieval
2002), readability (Si and Callan 2001), sentiment (Pang et al. 2002), and opinionatedness (Ounis et al. 2006). Although we do not explore all of these query independent features in this work, we do make use of several of them for a Web search task later in this chapter. 4 Examples Now that we have described each element that makes up the 3-tuple, we show how to construct MRFs from canonical forms. We do this by working through a number of examples. In all of the following examples, it is assumed that the query being evaluated is new york city.
Given a canonical form, it is easy to systematically build the corresponding MRF and derive both the joint probability mass function, as well as the ranking function. We represent all MRFs throughout the remainder of this work using these canonical forms. We now describe the meaning and details of each component in the 3-tuple. 1 Dependence Model Type The first entry in the tuple is the dependence model type, which specifies the dependencies, if any, that are to be modeled between query terms. As we described before, dependencies are encoded by the edges in the MRF, with different edge configurations correspond to different types of dependence assumptions.
Ad hoc retrieval is one of the most important information retrieval tasks. In the task, a user submits a query, and the system returns a ranked list of documents that are topically relevant to the query. Therefore, the goal of the task is to find topically relevant documents in response to a query. It is critical to develop highly effective ad hoc retrieval models since such models often play important roles in other retrieval tasks. For example, most QA systems use an ad hoc retrieval system to procure documents that are topically relevant to some question.
A Feature-Centric View of Information Retrieval by Donald Metzler