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A chart of accounts (COA) is a list of financial accounts and reference numbers, grouped into categories, such as assets, liabilities, equity, revenue and expenses, and used for recording transactions in the organization's general ledger. Accounts may be associated with an identifier (account number) and a caption or header and are coded by ...
For this problem, we are typically given a list of words and associated word senses, e.g. from a dictionary or from an online resource such as WordNet. Word-sense induction – open problem of natural-language processing, which concerns the automatic identification of the senses of a word (i.e. meanings). Given that the output of word-sense ...
Truecasing, also called capitalization recovery, [1] capitalization correction, [2] or case restoration, [3] is the problem in natural language processing (NLP) of determining the proper capitalization of words where such information is unavailable.
We are now reaching a sort of tipping point where we will see many more commercial applications of NLP — some using some of these open source, publicly available platforms — hit the market.
A natural-language search engine would in theory find targeted answers to user questions (as opposed to keyword search). For example, when confronted with a question of the form 'which U.S. state has the highest income tax?', conventional search engines ignore the question and instead search on the keywords 'state', 'income' and 'tax'.
Natural-language programming (NLP) is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English. [1] A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program. Natural language programming is not to be mixed up with ...
Natural language understanding (NLU) or natural language interpretation (NLI) [1] is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension.
Generative language models are not trained on the translation task, let alone on a parallel dataset. Instead, they are trained on a language modeling objective, such as predicting the next word in a sequence drawn from a large dataset of text. This dataset can contain documents in many languages, but is in practice dominated by English text. [36]