Explicit Semantic Analysis (ESA) google
In natural language processing and information retrieval, explicit semantic analysis (ESA) is a vectorial representation of text (individual words or entire documents) that uses a document corpus as a knowledge base. Specifically, in ESA, a word is represented as a column vector in the tf-idf matrix of the text corpus and a document (string of words) is represented as the centroid of the vectors representing its words. Typically, the text corpus is Wikipedia, though other corpora including the Open Directory Project have been used. ESA was designed by Evgeniy Gabrilovich and Shaul Markovitch as a means of improving text categorization and has been used by this pair of researchers to compute what they refer to as ‘semantic relatedness’ by means of cosine similarity between the aforementioned vectors, collectively interpreted as a space of ‘concepts explicitly defined and described by humans’, where Wikipedia articles (or ODP entries, or otherwise titles of documents in the knowledge base corpus) are equated with concepts. The name ‘explicit semantic analysis’ contrasts with latent semantic analysis (LSA), because the use of a knowledge base makes it possible to assign human-readable labels to the concepts that make up the vector space. ESA, as originally posited by Gabrilovich and Markovitch, operates under the assumption that the knowledge base contains topically orthogonal concepts. However, it was later shown by Anderka and Stein that ESA also improves the performance of information retrieval systems when it is based not on Wikipedia, but on the Reuters corpus of newswire articles, which does not satisfy the orthogonality property; in their experiments, Anderka and Stein used newswire stories as ‘concepts’. To explain this observation, links have been shown between ESA and the generalized vector space model. Gabrilovich and Markovitch replied to Anderka and Stein by pointing out that their experimental result was achieved using ‘a single application of ESA (text similarity)’ and ‘just a single, extremely small and homogenous test collection of 50 news documents’. Cross-language explicit semantic analysis (CL-ESA) is a multilingual generalization of ESA. CL-ESA exploits a document-aligned multilingual reference collection (e.g., again, Wikipedia) to represent a document as a language-independent concept vector. The relatedness of two documents in different languages is assessed by the cosine similarity between the corresponding vector representations.
http://…-explicit-semantic-analysis-esa-explained


P-Tree Programming google
We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we evaluate on a given problem. The error values from the evaluations are propagated through the prototype tree. We use them to update the probability distributions that determine the symbol choices of further instances. The iterative method is applied to several symbolic regression benchmarks from the literature. It outperforms standard Genetic Programming to a large extend. Furthermore, it relies on a concise set of parameters which are held constant for all problems. The algorithm can be employed for most of the typical computational intelligence tasks such as classification, automatic program induction, and symbolic regression. …

DeepPath google
We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets. …

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