A Survey on Context Learning <s> Link Based Analysis <s> Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers, the equivalent of "society" is "database," and the equivalent of "use" is "a way to search the database". We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts, we use the World Wide Web (WWW) as the database, and Google as the search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the WWW using Google page counts. The WWW is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87 percent with the expert crafted WordNet categories <s> BIB001 </s> A Survey on Context Learning <s> Link Based Analysis <s> This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide structured world knowledge about the terms of interest. Our approach is unique in that it does so using the hyperlink structure of Wikipedia rather than its category hierarchy or textual content. Evaluation with manually defined measures of semantic relatedness reveals this to be an effective compromise between the ease of computation of the former approach and the accuracy of the latter. <s> BIB002
The objective behind this class of methods is to determine the semantic similarity between two terms. For instance given two terms like "Diesel Engine" and "Ecology" or like "global warming" and "cars"  , one would want to know if they are semantically related or not. The answer to these questions despite being subjective are crucial to many IR systems and one would want to reason it out objectively and produce an output score denoting the similarity. In order to do so, unlike the N-gram that uses a window of immediate past elements/tokens, the methods in this class use external corpora, such as Wikipedia, as a means of building a knowledge-base. Methods in this category use the hyper-link structure defined inside the corpora as a measure of semantic relatedness. The primary motivation behind this is that the links provide a user-defined relationship between the terms present in the articles. Thus, methods in this class do not need to infer the context as the required context is explicitly defined by user annotation. While the final objective is to give the similarity score between the terms, the process usually involves series of smaller tasks: 1) Identifying the corresponding articles with respect to the input terms, 2) Weighting/scoring the links within those articles that connect them; thus, measuring the similarity between the articles, and 3) Finally using the above obtained score to calculate the similarity between the terms. The most important point to be discussed here is the part of weighting/scoring the links. There are two distinct approaches: (a) Adapting Term Frequency Â Inverse Document Frequency (TF Á IDF) to links. (b) Using normalized Google Distance. Adapting TF Á IDF. In  , the authors propose the Wikipedia Link Model (WLM). The WLM uses an idea similar to TF Á IDF by using link counts weighted by the probability of each link occurring as opposed to constructing vectors of term counts weighted by their probability of the term occurring. This probability is defined by the total number of links to the target article over the total number of articles. Thus, given two Wikipedia articles a and b, the weight w for link a ! b is given as where t is the total number of articles within Wikipedia, ja ! bj denotes the number of times the source article contains the link (generally 1 or 0). Equation (3) weights a link within a source article by the number of times the source article contains that link and by the inverse probability of any link to the target article. Thus links are considered less significant for judging the similarity between articles if many other articles also link to the same target. For instance if two articles both link to science then it is much less significant than if they both link to a specific topic such as atmospheric thermodynamics. Thus if there are n links-l 1 . . . l n , for a pair of articles x and y then the corresponding vector representations are Once the vectors are created, the similarity between them can be measured using any of the established similarity measures that can accept these weights as parameters. Normalized Google Distance. The authors of BIB002 take a different approach in measuring the similarity between the articles. They use a method analogous to Normalized Google Distance BIB001 . The basic idea is that pages that contain both terms indicate relatedness, while pages with only one of the terms suggest the opposite. The authors modify this measure to suit Wikipedia's links rather than Google's search results. Formally, the measure is defined as similarityða; bÞ ¼ logðmaxðjAj; jBjÞÞ À logðjA \ BjÞ logðjW jÞ À logðminðjAj; jBjÞÞ ; where a and b are the two articles of interest, A and B are the sets of all articles that link to a and b respectively, W is the entire Wikipedia. It has been shown that the combination of these two methods perform better as shown in Table 1 .