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Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, [1] is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing.
Teragram is based in Cambridge, Massachusetts and specializes in the application of computational linguistics to multilingual natural-language processing. TipTop Technologies – company that developed TipTop Search, a real-time web, social search engine with a unique platform for semantic analysis of natural language.
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU.
The methods were; comprehension evaluation, quality panel evaluation, and evaluation based on adequacy and fluency. Comprehension evaluation aimed to directly compare systems based on the results from multiple choice comprehension tests, as in Church et al. (1993). The texts chosen were a set of articles in English on the subject of financial news.
One method for scaling up test-time compute is process-based supervision, where a model generates a step-by-step reasoning chain to answer a question, and another model (either human or AI) provides a reward score on some of the intermediate steps, not just the final answer. Process-based supervision can be scaled arbitrarily by using synthetic ...
The simpler of these measures, WER, is simply the percentage of erroneously recognized words E (deletions, insertions, substitutions) to total number of words N, in a speech recognition task i.e. = % The second metric, perplexity (per token), is an information theoretic measure that evaluates the similarity of proposed model m to the original ...
Edge-based: which use the edges and their types as the data source; Node-based: in which the main data sources are the nodes and their properties. Other measures calculate the similarity between ontological instances: Pairwise: measure functional similarity between two instances by combining the semantic similarities of the concepts they represent