A Survey on Context Learning <s> Explicit Semantic Analysis <s> Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared with the previous state of the art, using ESA results in substantial improvements in correlation of computed relatedness scores with human judgments: from r = 0.56 to 0.75 for individual words and from r = 0.60 to 0.72 for texts. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users. <s> BIB001 </s> A Survey on Context Learning <s> Explicit Semantic Analysis <s> Berners-Lee's compelling vision of a Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method - creating enough structured data to motivate the development of applications. This paper argues that autonomously "Semantifying Wikipedia" is the best way to solve the problem. We choose Wikipedia as an initial data source, because it is comprehensive, not too large, high-quality, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. We identify several types of structures which can be automatically enhanced in Wikipedia (e.g., link structure, taxonomic data, infoboxes, etc.), and we describea prototype implementation of a self-supervised, machine learning system which realizes our vision. Preliminary experiments demonstrate the high precision of our system's extracted data - in one case equaling that of humans. <s> BIB002 </s> A Survey on Context Learning <s> Explicit Semantic Analysis <s> Name ambiguity problem has raised an urgent demand for efficient, high-quality named entity disambiguation methods. The key problem of named entity disambiguation is to measure the similarity between occurrences of names. The traditional methods measure the similarity using the bag of words (BOW) model. The BOW, however, ignores all the semantic relations such as social relatedness between named entities, associative relatedness between concepts, polysemy and synonymy between key terms. So the BOW cannot reflect the actual similarity. Some research has investigated social networks as background knowledge for disambiguation. Social networks, however, can only capture the social relatedness between named entities, and often suffer the limited coverage problem. To overcome the previous methods' deficiencies, this paper proposes to use Wikipedia as the background knowledge for disambiguation, which surpasses other knowledge bases by the coverage of concepts, rich semantic information and up-to-date content. By leveraging Wikipedia's semantic knowledge like social relatedness between named entities and associative relatedness between concepts, we can measure the similarity between occurrences of names more accurately. In particular, we construct a large-scale semantic network from Wikipedia, in order that the semantic knowledge can be used efficiently and effectively. Based on the constructed semantic network, a novel similarity measure is proposed to leverage Wikipedia semantic knowledge for disambiguation. The proposed method has been tested on the standard WePS data sets. Empirical results show that the disambiguation performance of our method gets 10.7% improvement over the traditional BOW based methods and 16.7% improvement over the traditional social network based methods. <s> BIB003 </s> A Survey on Context Learning <s> Explicit Semantic Analysis <s> This paper summarizes our recent research on semantic clustering of scientific articles. We present a case study which was focused on analysis of papers related to the Rough Sets theory. The proposed method groups the documents on the basis of their content, with an assistance of the DBpedia knowledge base. The text corpus is first processed using Natural Language Processing tools in order to produce vector representations of the content. In the second step the articles are matched against a collection of concepts retrieved from DBpedia. As a result, a new representation that better reflects the semantics of the texts, is constructed. With this new representation the documents are hierarchically clustered in order to form a partitioning of papers into semantically related groups. The steps in textual data preparation, the utilization of DBpedia and the employed clustering methods are explained and illustrated with experimental results. A quality of the resulting clustering is then discussed. It is assessed using feedback form human experts combined with typical cluster quality measures. These results are then discussed in the context of a larger framework that aims to facilitate search and information extraction from large textual repositories. <s> BIB004
The name Explicit Semantic Analysis is borrowed from the pioneering work by Gabrilovich in BIB001 . Similar to link based analysis, the ESA uses external resource such as Wikipedia as a form of knowledge-base. While the motivation of this method was to compute semantic relatedness between terms in a given query, it has been adapted to other tasks such as Named Entity Disambiguation BIB003 , document clustering BIB004 , automatic "semantifying" BIB002 , etc. However, regardless of the task there is a common underlying theme or step that can be pictorially represented-refer Fig. 1 . As can be observed in Fig. 1 , each article in the Wikipedia is parsed, tokenized, pre-processed (stop-word removal, lemmatization, etc.) and an inverted index is built. The inverted index is essentially a key-value pair wherein the key is each token encountered in an article and the value is the set of articles in which the key has appeared. It is possible to associate weights to each element in the value set to represent the strength of association between the token (key) and the Wikipedia article (an entry in the value set). This knowledge-base is used to create "interpretation vectors" for each query term. An "interpretation vector" is a weighted vector of Wikipedia articles that combines the weight from inverted index as well as the weight of the corresponding token in the text. Usually the weights of the tokens in the text are computed using TF Á IDF approach (Term Frequency Â Inverse Document Frequency). For computation purposes, each query is regarded as a document. For short text the TF is usually 1 and the weight of term i, denoted by v i , in the input text is then solely determined by IDF. For online processing, as all the query terms are not available in advance, one is usually constrained to go with only TF and incrementally build the Document Frequency (DF). If the weight of an article j for term i from inverted index is represented as k j , then for all the words in the query text, the aggregate weight w.r.t. article j can be calculated as where T ¼ fw i g is the input text. Thus, using m j of each Wikipedia page, we create the "interpretation vectors", which are then used to measure similarity using methods like Cosine similarity. As opposed to N-gram, the ESA uses the entire Wikipedia article as a context to a given query. Since the analysis involves manipulation of concepts (Wikipedia articles) which are explicitly represented in high dimensional feature spaces using the inverted index, the authors of BIB001 have named this approach as "Explicit Semantic Analysis". Here is an example. Consider the input: U.S. intelligence cannot say conclusively that Saddam Hussein has weapons of mass destruction, an information gap that is complicating White House efforts to build support for an attack on Saddam's Iraqi regime. The CIA has advised top administration officials to assume that Iraq has some weapons of mass destruction. But the agency has not given President Bush a "smoking gun", according to U.S. intelligence and administration officials. Assuming an inverted index is built and available for querying, a set of Wikipedia pages corresponding to number of tokens covered in the input and their corresponding weights are obtained. Using Equation (6), a ranked set of concepts in the "interpretation vector" that best describes the input is as shown in Table 2 .