# Text Semantic Annotation: A Distributed Methodology Based on Community Coherence
## 2 Background
### Word Sense Disambiguation Algorithms
A similar technique was followed by [14], using an equivalent graph representation, mentioned as the Referent Graph, which presented corresponding primitives with the Mention-Entity Graph, but formed the disambiguation process core based on the PageRank algorithm. Another approach in [15] introduces the HITS algorithm over a sub-graph of the RDF Knowledge Base, using a pruned breadth-first search traversal. The entities in the disambiguated text are used as initial seed nodes for the graph traversal. The system performance is achieved by the exploitation of coreference resolution heuristics, normalization, and expansion of surface forms. In WAT [16], the successor of [12], the three-step architecture components were further refined and redesigned. Several methodology variations have been evaluated experimentally, including some basic graph-based methodologies, including PageRank and HITS. Individual experimental evaluations for each step of the disambiguation pipeline are included in the results, evaluating the performance of each individual component. In [17], one of the first deep neural network models was introduced. The proposed methodology encodes representations of both mention, context, and entity in a continuous vector space. Then, a convolutional neural network models variable size context windows, embedding context word points on the vector space, and a neural tensor network is used for semantic modeling of context and mention interactions, in a compute-intensive process. Ref. [18] also proposed an embedding method, mapping words and entities into the same continuous vector space. They extended the skip-gram model via a KB graph, and an anchor context model aimed to align vectors such that similar words and entities occurred close to one another in the vector space. Another deep learning model was introduced in [19] employing neural representations. Entity embeddings, along with a \"neural attention mechanism\" over variable-sized context frames, are combined with a joint inference stage for the disambiguation step, in an approach attempting to combine some deep learning benefits with more traditional approaches such as probabilistic mention-entity maps and graphical models. Apart from the most prominent research works outlined above, several similar endeavors in recent years approached Entity Linking and relevant AI tasks using deep neural network architectures, and faced similar challenges. However, vast dimensionality reduction due to the input dimension is required, introducing significant sense granularity deterioration at the input level as outlined in [20]. Furthermore, several recent works such as [21] question context-dependent deep neural network EL approaches in real-world big-data industry applications, as current methods focus increasingly on quality performance rather than run-time. To that end, RedW2 takes a step further back, introducing a context-free end-to-end Wikification system only utilizing knowledge of Wikipedia redirect pages, and outlining the feasibility of deep neural network approaches at scale.