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Noisy text analytics is a process of information extraction whose goal is to automatically extract structured or semistructured information from noisy unstructured text data. While Text analytics is a growing and mature field that has great value because of the huge amounts of data being produced, processing of noisy text is gaining in ...
Machine learning techniques, either supervised or unsupervised, have been used to induce such rules automatically. Wrappers typically handle highly structured collections of web pages, such as product catalogs and telephone directories. They fail, however, when the text type is less structured, which is also common on the Web.
Flex (fast lexical analyzer generator) is a free and open-source software alternative to lex. [2] It is a computer program that generates lexical analyzers (also known as "scanners" or "lexers").
Another method [8] is to build the parse forest as you go, augmenting each Earley item with a pointer to a shared packed parse forest (SPPF) node labelled with a triple (s, i, j) where s is a symbol or an LR(0) item (production rule with dot), and i and j give the section of the input string derived by this node. A node's contents are either a ...
Unstructured information can then be enriched and tagged to address ambiguities and relevancy-based techniques then used to facilitate search and discovery. Examples of "unstructured data" may include books, journals, documents, metadata , health records , audio , video , analog data , images, files, and unstructured text such as the body of an ...
Web scraping is the process of automatically mining data or collecting information from the World Wide Web. It is a field with active developments sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, semantic understanding, artificial intelligence and human-computer interactions.
Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
Popular search engines focus on the full-text indexing of online, natural language documents. [1] Media types such as pictures, video, [2] audio, [3] and graphics [4] are also searchable. Meta search engines reuse the indices of other services and do not store a local index whereas cache-based search engines permanently store the index along ...