# Text Semantic Annotation: A Distributed Methodology Based on Community Coherence
## 3 Materials and Methods
### Disambiguation  
Following the anchor extraction, the disambiguation process is involved in the resolution and selection of the best mention from \(P_{\mathcal{G}}(a)\) for polysemous anchors identified in the input text segment. Unambiguous anchors may bare a single entity on their \(P_{\mathcal{G}}(a)\) set, hence can be excluded from this process. The intuition behind the novel methodologies of our contribution stems from the interpretation of Wikipedia using graph representation. We employ a novel scoring schema for the evaluation of candidate mentions of an anchor in a local context window. In general, it is anticipated that entities expressing a high degree of reciprocity would be also members of the same community. So far,the exploration of relatedness metrics among Wikipedia entities has focused on the intersection of two articles' incoming links. As a result, transitive or deeper relations are neglected by the ranking process. The evaluation score of each mention of an anchor during the disambiguation process is carried out by employing a Gaussian Process binary Classifier (GPC) [22] which was selected in terms of interpretability for assessing our methodology. Particularly, we model the entity linking task by performing binary classification on each probable mention of an anchor, for deriving probabilistic classification scores to the two obvious classes:  
* the _entity linking compatibility_ and
* the _entity linking incompatibility_  
Thus, the predictions take the form of class probabilities. For our GPC implementation, the classifier approximates the non-Gaussian posterior with a Gaussian based on the Laplace approximation as detailed in Chapter 3 of [22]. Our classifier implementation used a Radial basis function kernel in all cases. Following the terminology and notation presented in Section 3.1, the classification process is based on the following features:  
\[lp(a)=\frac{link(a)}{freq(a)} \tag{1}\]  
\[\text{\emph{community coherence}}=1-\frac{\{max(|C|):\,\text{a}_{0\_k}\in C \,\}}{k+1} \tag{2}\]  
\[\text{\emph{avg interwiki jaccard index}}(a_{i})=\sum\
olimits_{k=0}^{k=i-1} \frac{\left|\text{in}(\text{p}_{a_{i}})\cap\text{in}(\text{p}_{a_{k}})\right|} {\left|\text{in}(\text{p}_{a_{i}})\cup\text{in}(\text{p}_{a_{k}})\right|}/ \text{m}+\sum\
olimits_{k=i+1}^{k=m}\frac{\left|\text{in}(\text{p}_{a_{i}}) \cap\text{in}(\text{p}_{a_{k}})\right|}{\left|\text{in}(\text{p}_{a_{i}}) \cup\text{in}(\text{p}_{a_{k}})\right|}/\text{m} \tag{3}\]  
\[\text{\emph{avg relatedness}}(a_{i})=\sum\
olimits_{k\in[\text{p}_{a_{0}}-\text{p }_{a_{m}}]-[\text{p}_{a_{i}}]}\frac{\log\left(\max(\left|\text{in}(\text{p}_{ a_{i}})\right|,\left|\text{in}(\text{p}_{a_{k}})\right|)-\log\left(\left|\text{in}( \text{p}_{a_{i}})\cap\text{in}(\text{p}_{a_{k}})\right|)\right)}{\log\left( \left|W\right|\right)-\log\left(\min(\left|\text{in}(\text{p}_{a_{i}})\right|, \left|\text{in}(\text{p}_{a_{k}})\right|)\right)}/\text{m} \tag{4}\]  
Essentially:
* The _community coherence_ presented by Formula (2) is used for modeling the coherence of mentions as members of common communities. * The _average interwiki Jaccard index_ of Formula (3) models the average intersection over the union of entity mentions in a text segment for annotation. This feature aims at introducing strong semantic coherence information. The Jaccard index is established by similar works such as WAT [16]. * The feature of _relatedness_, which generally models the Wikipedia entity semantic relevance, was introduced in [9] and broadly used in several works [9; 10; 11; 12; 16]. The generalized form of the _average relatedness_ as proposed in (4) is the last feature for our binary classification model. The binary output classes defined reflect the positive and negative linking compatibility within the current context based on the input features. The classifier is used for scoring each available mention of an anchor. The mention with the highest positive linking compatibility is selected at the final step.