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To this aim we selected 102,000 pairs of words (chosen randomly from the 1500 more frequent nouns) and computed their similarity Tyrosine Kinase Inhibitor Library based on Wordnet, LSA, and TSS. TSS showed a very strong correlation of word similarity to LSA (�� = 0.2199, p = 0) and to several measures based on Wordnet: (1) shortest path that connects senses using hypernym and hyponym relation (�� = 0.298, p VAV2 dTSSw, dLSAw of 26 components each. If we sort dw in increasing order, semantic measure should show the w-synonym words last in the list; that is, most similar words, higher similarity values, should be the synonyms. Mean position of synonyms in dLSAw showed ?dLSAw? = 0.602 �� 0.328, in concordance with previous results that state 60% of performance in TOEFL vocabulary exam [28]. With this result, synonyms are close to the middle in the ordered list, and so TOEFL exam should fail. On the other hand, for TSS we obtained that dTSSw showed a higher value, ?dTSSw? = 0.871 �� 0.204, which is significantly higher than the null hypothesis (the uniform distribution over the interval) and the results obtained with LSA. Second, we verified that typical and easily recognizable semantic clusters are well described by TSS. To this aim we generated 1000 sets Lonafarnib manufacturer of 12 words belonging to three semantic categories: fruits, animals, and colors (see methods for the complete list of words in each category). For each set, we calculated the TSS similarity submatrix and run a 10-fold cross-validation K? classifier [23]. Performance for TSS was very well above chance (K? classification of chance generated groups is approximately 0.3 �� 0.0001) with values of 0.6846 �� 1.6458 �� 10?4 (on the same dataset, 0.5543 �� 1.8265 �� 10?4 for LSA and 0.6736 �� 1.5419 �� 10?4 for Wordnet). To exemplify the capacity of TSS to cluster words in semantic categories, we used multidimensional scaling (MDS) [29] to project the semantic network to the 2-dimensional plane (Figure 1(a)). This representative example shows that words belonging to the same category cluster together. This particular example shows finesse of the metrics above and beyond classifying in broad categories. For instance, animals are subdivided in two natural categories (cat-dog and horse-cow).