You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Context Learning 
Chapter Title:  Ensemble Based Analysis 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  Wikification, commonly referred to as Disambiguation to Wikipedia (D2W), is the task of identifying concepts and entities in text and disambiguating them into the most specific corresponding Wikipedia pages. Previous approaches to D2W focused on the use of local and global statistics over the given text, Wikipedia articles and its link structures, to evaluate context compatibility among a list of probable candidates. However, these methods fail (often, embarrassingly), when some level of text understanding is needed to support Wikification. In this paper we introduce a novel approach to Wikification by incorporating, along with statistical methods, richer relational analysis of the text. We provide an extensible, efficient and modular Integer Linear Programming (ILP) formulation of Wikification that incorporates the entity-relation inference problem, and show that the ability to identify relations in text helps both candidate generation and ranking Wikipedia titles considerably. Our results show significant improvements in both Wikification and the TAC Entity Linking task.

Literature Review:
All the aforementioned techniques exploit only one of the many possible explicit features. For instance in Section 2.2, we present an approach that models the knowledge-base as a graph and uses the explicit link structure defined in it. As opposed to this, method described in Section 2.3 makes use of the tokens in the text to build an inverted index and measure similarity between concepts. In this section, we present some broad text based algorithms that combine a host of features, mainly explicit in nature. Continuing our application domain of text, we now introduce another application-Wikification, that has a real world implications and also entails previously introduced tasks of semantic relatedness. The objective of Wikification is to identify the tokens in a given text that are important in the given context and link them to an entry in an external repository such as a Wikipedia. While the N-gram model in Section 2.1 can help in determining whether a token (a.k.a mention) in the text needs to be linked or not and techniques in Sections 2.2 and 2.3 can help in determining the semantic relatedness, the actual task of linking goes much further than that and is more complicated. A generic formulation of this problem is as follows: If we are given a document d with a set of mentions M = {m 1 ; m 2 ; m 3 ; . . . ; m N g, and a collection of Wikipedia articles W = {t 1 ; . . . ; t jW j g, then the output of Wikification is a mapping between the set M and W denoted by G. The objective of Wikification BIB001 , BIB002 is to identify the best set of mapping, G Ã , such that where f is a function that measures the local contextual similarity between a mention and a Wikipedia article and c is a global contextual similarity function to ensure that all the selected links, i.e., disambiguations, form a "coherent" set of related concepts. In order to compute f and c, works like BIB003 , BIB002 use a host of features like ESA, PMI and Normalized Google Distance of the inlinks and outlinks, etc. (refer Fig. 2 ) to train a classifier to decide the mappings. As shown in Table 4 , this ensemble of explicit features yield a better result.