The Stanford Wordnet Project     It is a long-standing dream of AI to have algorithms automatically read and obtain knowledge from text. By applying a learning algorithm to parsed text, we have developed methods that can automatically identify the concepts in the text and the relations between them. For example, reading the phrase "heavy water rich in the doubly heavy hydrogen atom called deuterium", our algorithm learns (and adds to its semantic network) the fact that deuterium is a type of atom (Snow et al., 2005). significantly enhanced versions of WordNetautomatically inducing knowledge to add to WordNet, our work provides an even greater NLP resource (e.g., significantly greater precision/recall in identifying various relations), but at a tiny fraction of the cost.     http://ai.stanford.edu/~rion/swn/index.html     tags: wordnet The Stanford Wordnet Project     These lexical resources (and the method of their construction) are described in Semantic Taxonomy Induction from Heterogenous Evidence (ACL-06). They are automatically augmented versions of WordNet 2.1 (available at http://wordnet.princeton.edu).     http://ai.stanford.edu/~rion/swn/augmented.html     tags: wordnet ConceptNet     What is ConceptNet? [top] ConceptNet is a freely available commonsense knowledgebase and natural-language-processing toolkit which supports many practical textual-reasoning tasks over real-world documents right out-of-the-box (without additional statistical training) including ...     http://web.media.mit.edu/~hugo/conceptnet/     tags: nlp cyc wordnet python