Search Result Clustering Using Label Language Model
نویسندگان
چکیده
Search results clustering helps users to browse the search results and locate what they are looking for. In the search result clustering, the label selection which annotates a meaningful phrase for each cluster becomes the most fundamental issue. In this paper, we present a new method of using the language modeling approach over Dmoz for label selection, namely label language model. Experimental results show that our method is helpful to obtain meaningful clustering labels of search results.
منابع مشابه
Japanese Abbreviation Expansion with Query and Clickthrough Logs
A novel reranking method has been developed to refine web search queries. A label propagation algorithm was applied on a clickthrough graph, and the candidates were reranked using a query language model. Our method first enumerates query candidates with common landing pages with regard to the given query to create a clickthrough graph. Second, it calculates the likelihood of the candidates, usi...
متن کاملA New Approach to Search Result Clustering and Labeling
A NEW APPROACH TO SEARCH RESULT CLUSTERING AND LABELING Anıl Türel M.S. in Computer Engineering Supervisor: Prof. Dr. Fazlı Can August, 2011 Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of information retrieval results for easier access to the desired information is an important research problem. A post-processing techni...
متن کاملAn improved opposition-based Crow Search Algorithm for Data Clustering
Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...
متن کاملAn Algorithm for Clustering of Web Search Results
In this thesis we propose a description-oriented algorithm for clustering of results obtained from Web search engines called LINGO. The key idea of our method is to first discover meaningful cluster labels and then, based on the labels, determine the actual content of the groups. We show how the cluster label discovery can be accomplished with the use of the Latent Semantic Indexing technique. ...
متن کاملClustering novel intents in a conversational interaction system with semantic parsing
Spoken language understanding (SLU) in today’s conversational systems focuses on recognizing a set of domains, intents, and associated arguments, that are determined by application developers. User requests that are not covered by these are usually directed to search engines, and may remain unhandled. We propose a method that aims to find common user intents amongst these uncovered, out-of-doma...
متن کامل