A Survey on Context Learning <s> Discussion on Explicit Analysis Methods <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> Discussion on Explicit Analysis Methods <s> This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles. The resulting link detector and disambiguator performs very well, with recall and precision of almost 75%. This performance is constant whether the system is evaluated on Wikipedia articles or "real world" documents. This work has implications far beyond enriching documents with explanatory links. It can provide structured knowledge about any unstructured fragment of text. Any task that is currently addressed with bags of words - indexing, clustering, retrieval, and summarization to name a few - could use the techniques described here to draw on a vast network of concepts and semantics. <s> BIB002 </s> A Survey on Context Learning <s> Discussion on Explicit Analysis Methods <s> Disambiguating concepts and entities in a context sensitive way is a fundamental problem in natural language processing. The comprehensiveness of Wikipedia has made the online encyclopedia an increasingly popular target for disambiguation. Disambiguation to Wikipedia is similar to a traditional Word Sense Disambiguation task, but distinct in that the Wikipedia link structure provides additional information about which disambiguations are compatible. In this work we analyze approaches that utilize this information to arrive at coherent sets of disambiguations for a given document (which we call "global" approaches), and compare them to more traditional (local) approaches. We show that previous approaches for global disambiguation can be improved, but even then the local disambiguation provides a baseline which is very hard to beat. <s> BIB003 </s> A Survey on Context Learning <s> Discussion on Explicit Analysis Methods <s> This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known variations of the Lesk WSD method. Given a word and its context, Lesk algorithm exploits the idea of maximum number of shared words (maximum overlaps) between the context of a word and each definition of its senses (gloss) in order to select the proper meaning. The main contribution of our approach relies on the use of a word similarity function defined on a distributional semantic space to compute the gloss-context overlap. As sense inventory we adopt BabelNet, a large multilingual semantic network built exploiting both WordNet and Wikipedia. Besides linguistic knowledge, BabelNet also represents encyclopedic concepts coming from Wikipedia. The evaluation performed on SemEval-2013 Multilingual Word Sense Disambiguation shows that our algorithm goes beyond the most frequent sense baseline and the simplified version of the Lesk algorithm. Moreover, when compared with the other participants in SemEval-2013 task, our approach is able to outperform the best system for English. <s> BIB004
As mentioned earlier, Explicit Analysis methods yield high accuracy, but they are limited in capability as the performance is tied to the knowledge-base created. Table 3 compares the ESA with link based approach. As can be seen ESA is the better of the two approaches. It is less brittle, because it only requires that articles mention the terms involved. WLM, however, achieves competitive levels of accuracy when the terms involved correspond to distinct Wikipedia articles. ESA is able to determine the relatedness of texts of any length as easily as individual terms. This is possible due to inverted index created and the merging of the lists of articles related to each word. WLM on the other hand is not so easily extended and requires another intermediary step of Wikification BIB001 to discover the topics mentioned in the texts. However, the best result is achieved when one integrates the different contributions brought by these approaches. Table 4 BIB003 shows the superiority of the ensemble approach as compared to a simple conditional probability based approach (simulating an N-gram approach) and a modification of WLM framework BIB002 in doing Wikification task. Explicit analysis are particularly useful when one has access to large amount of data; thus, allowing construction of a knowledge-base. While this class of technique provides us with an ability to analyze data and yield a ready real world interpretation, the following sections, discuss methodology that strives to reason and understand the underlying interaction between concepts, manifested through various surface forms that the explicit analysis techniques eventually use. Towards this end, much efforts are now employed in connecting these two classes of analyses to understand the semantic connection between latent and observed variables for performing tasks like word sense disambiguation BIB004 and topic modelling.