## 5 The Determination of the Weights
As metrics, we adopted _Precision_ (_P_), _Recall_ (_R_), and _F-measure_ (_F_) [4], defined as follows:

\[P=\frac{\mbox{number of correct suggestions returned}}{\mbox{number of suggestions returned}}=\frac{\mbox{true positive}}{\mbox{true positive }+\mbox{false positive}} \tag{12}\]

\begin{table}
\begin{tabular}{l|c} \hline \hline _The price of the bow was as much as the_ _income of a common man for a year._ \\ \hline n. of subsequences: 7 & \\ n. of clusters: 2 & \\ Xie-Beni index: 0.00015013 & \\ \(f\) = 47,763 & \\ \hline \(n\)-grams & \(\left|\!\left|G\right|\!\right|\!\right|_{\!\! \[R=\frac{number\text{ \ of \ correct suggestions \ returned}}{total\text{ \ number of \ errors in the collection}}=\frac{true\text{ \ positive}}{true\text{ \ positive}+\text{\ false negative}} \tag{13}\]

\[F=2\,\frac{P\cdot R}{P+R} \tag{14}\]

Precision is a measure of exactness or quality, whereas recall is a measure of completeness or quantity. The F-measure combines precision and recall via the harmonic mean of them. Table 5 and Table 6 show the confusion matrix and the system performance, respectively. Both recall and precision are very high, thus confirming the effectiveness of our method. ## 7 Conclusions and Future Works

In this paper we presented a novel approach to text analysis able to overcome the designer-dependent representations of the available analyzers, which are more efficient but work as long as the system does not have to stray too far from the conditions under which these explicit representations were formulated. By using an emergent paradigm, in our approach interactions between words in the Web can be represented in terms of visual properties of the input text. In contrast, both symbolic and statistical approaches are cognitivist, involving a representation of a given pre-determined linguistic objective, established based on domain knowledge acquisition in the design process. Hence, cognitivist approaches are characterized by efficiency in solving specific application problems with more or less adaptability, in contradistinction with the emergent approach, which is characterized by embodiment, adaptation, autonomy, and self-organization. Our approach to text analysis is based on the principles of connectionism and embodiment with the environment. The system employs hit counts and snippets provided by web search engines, in order to rate the subsequences of the input sentence, thus producing usage relationships between words of the sentence. A connectionist structure is then built to represent and optimize such relationships, via an unsupervised fuzzy clustering process. Finally, a visual output of the sentence is provided, with usage \begin{table}
\begin{tabular}{c|c|c} \hline P & R & F \\ \hline
0.94 & 0.88 & 0.91 \\ \hline \end{tabular}
\end{table} 
Table 6: Performance of the system. \begin{table}
\begin{tabular}{c|c|c} \hline \hline  & \multicolumn{2}{c}{**Actual class (observation)**} \\ \hline  & 44 & 3 \\  & True positive & False positive \\  & (wrong sentence, atypical & (good sentence, atypical \\  & subsequence discovered) & subsequence discovered) \\ \cline{2-3}  & 6 & 27 \\  & False negative & True negative \\  & (wrong sentence, & (good sentence, \\  & nothing discovered & nothing discovered) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix. information. The system has been discussed and tested on a collection of sentences of the British National Corpus, showing its effectiveness in highlighting real-world spelling errors. Work is underway to improve the match between word segments and snippets, and to test the system with other languages. Indeed, the approach is completely grammarless and open-world, thus providing an efficient means of analysis of sublanguages in the Web. Moreover, a more usable and manageable version of the system is under development, to allow performing beta tests and collecting assessments of linguistics experts. Finally, a challenge for the future lies in studying the possibility of integration of our method with other web-based models.