# Text Semantic Annotation: A Distributed Methodology Based on Community Coherence
## 1 Introduction  
Word Sense disambiguation is the process of identifying the sense of a textual segment of a sentence. The task deterministically yields a unique mapping to an entity, usually drawn from a concept ontology. Semantic ambiguity is a common property of natural language corpora, hence superficial approaches like the classic bag of words paradigm is often proven insufficient. To that end, word sense disambiguation and text annotation is a common pre-processing step of several information retrieval and natural language processing tasks, such as machine-translation, text summarization, natural language inference, taxonomy learning, searching, clustering, classification, argumentation mining, and question answering, with a major impact on several industries, including artificial intelligence, advertising, and the Internet.  
Many recent approaches focus on employing deep neural network architectures, requiring substantial reduction of the training input dimensionality due to their computational challenges for large-scale datasets. However, disambiguation accuracy is largely incommensurate in this computational complexity tradeoff. Statistical dependencies are projected to a lower degree space at the consequence of input accuracy loss. In addition, well-established features in domain knowledge are neglected and delegated for inference during the training phase. The high computational requirementsand lack of interpretability of deep neural network approaches have been inhibiting factors for greater adoption.  
In recent years, graph analysis techniques have been broadly adopted due to their supreme performance and their inherent efficiency in handling hierarchical data. From sociology, chemistry, and biology, to finance and computer science, the application of graph processing methods has proven beneficial in revealing the dynamics and the implicit correlations between information entities.  
As regards to recommendation systems, social influence analysis, digital marketing, etc., community detection [1] has proven essential in partitioning the original data graph to densely intra-connected subgroups of highly similar entities--that is, communities--by solely leveraging the network topology. Specifically, by recursively calculating a repetitively modified set of network topology metrics such as the edge/node connectivity, the clustering coefficient, the edge betweenness, and the centrality, the community detection algorithms manage to accurately reveal the underlying community structure for any possible information network.  
In this paper, a novel distributed methodology based on the community detection of semantic ontologies is introduced in terms of efficient word-sense disambiguation. In particular, by following the divide-and-conquer paradigm, this approach focuses on partitioning the sources of knowledge, expressed as an information network, using a community detection algorithm to identify the densely intra-connected communities of semantic ontologies.  
Thorough experimentation has been conducted on Wikipedia's source of knowledge using either the Louvain [2] or the Clauset-Newman-Moore [3] community detection algorithms, which are both considered golden standards in revealing the subjacent community structure. As practically shown, the accuracy and the relevance of the extracted documents are unquestionably ameliorated by focusing the input queries to a finer and far more precise subset of semantic ontologies.  
The remainder of this manuscript is organized as follows. In Section 2 the necessary background is presented. In Section 3 the proposed methodology and its implementation are comprehensively analyzed. In Section 4 the experimentation process is described and assessed. Finally, in Section 5 the final conclusions accompanied by some potential improvements are outlined.