Automatic discovery and ranking of synonyms for search keywords in the web

Abstract

Search engines are an indispensable part of a web user's life. A vast majority of these web users experience difficulties caused by the keyword-based search engines such as inaccurate results for queries and irrelevant URLs even though the given keyword is present in them. Also, relevant URLs may be lost as they may have the synonym of the keyword and not the original one. This condition is known as the polysemy problem. To alleviate these problems, we propose an algorithm called automatic discovery and ranking of synonyms for search keywords in the web (ADRS). The proposed method generates a list of candidate synonyms for individual keywords by employing the relevance factor of the URLs associated with the synonyms. Then, ranking of these candidate synonyms is done using co-occurrence frequencies and various page count-based measures. One of the major advantages of our algorithm is that it is highly scalable which makes it applicable to online data on the dynamic, domain-independent and unstructured World Wide Web. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard.

A Hybrid Approach to Discover Entity Synonyms

“…A pseudo document similarity function ensures the higher recall without dropping the precision. Srikantiah et al (2013) have proposed a mechanism to find the synonyms from the web based on inbound anchor text. They have used Search Engine Result Pages (SERPs) to find candidate synonyms of individual keywords.…”

Automatically refining synonym extraction results: Cleaning and ranking

“…Their approach is that of a logistic regression, and the extracted synonyms are scored and ranked in the extraction process. Srikantaiah et al [28] proposed an automatic discovery approach, whereby synonyms are ranked according to the keywords searched on the web. In order to rank the extracted synonyms, the similarity measures based on co-occurrence frequencies and page counts were employed, which were highly dependent on the specific features used in their synonym extraction approach.…”