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:  Discussion on Composite Models 
1. Abstract of Cited Paper (BIB001):  This paper introduces a new neural network language model (NNLM) based on word clustering to structure the output vocabulary: Structured Output Layer NNLM. This model is able to handle vocabularies of arbitrary size, hence dispensing with the design of short-lists that are commonly used in NNLMs. Several softmax layers replace the standard output layer in this model. The output structure depends on the word clustering which uses the continuous word representation induced by a NNLM. The GALE Mandarin data was used to carry out the speech-to-text experiments and evaluate the NNLMs. On this data the well tuned baseline system has a character error rate under 10%. Our model achieves consistent improvements over the combination of an n-gram model and classical short-list NNLMs both in terms of perplexity and recognition accuracy. 
2. Abstract of Cited Paper (BIB002):  Title matching refers roughly to the following problem. We are given two strings of text obtained from different data sources. The texts refer to some underlying physical entities and the problem is to report whether the two strings refer to the same physical entity or not. There are manifestations of this problem in a variety of domains, such as product or bibliography matching, and location or person disambiguation. ::: ::: We propose a new approach to solving this problem, consisting of two main components. The first component uses Web searches to "enrich" the given pair of titles: making titles that refer to the same physical entity more similar, and those which do not, much less similar. A notion of similarity is then measured using the second component, where the tokens from the two titles are modelled as vertices of a "social" network graph. A "strength of ties" style of clustering algorithm is then applied on this to see whether they form one cohesive "community" (matching titles), or separately clustered communities (mismatching titles). Experimental results confirm the effectiveness of our approach over existing title matching methods across several input domains.

Literature Review:
As each individual category of context learning has its pros and cons, we would like to combine them in certain scenarios: a task is able to be divided into several sub-tasks that can be very effectively solved by the individual techniques; we need a compromise model between quality and efficiency which gains satisfying performance with acceptable time consumption; both local context and global context can work complementarily so that the composite model performs better than one of them working alone; useful external information can be incorporated as prior knowledge to enhance the original model. For instances, BIB003 divides the named entity disambiguation problem into explicit title enrichment and implicit community detection problems, and conquer them separately; BIB001 , BIB002 combine neural network language models with explicit n-gram models to achieve a trade-off between computational cost and model performance. Composite models can be used to solve many practical problems as they can make use of both methods. However, most of the existing composite models utilize each individual method separately-either each individual method works in different stage or the outcome of one method is used as prior knowledge. In the future, composite models with deep cooperation among its individual methods would be very helpful.