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Stop-words
Stop words are the words in a stop list (or ''stoplist'' or ''negative dictionary'') which are filtered out (i.e. stopped) before or after processing of natural language data (text) because they are insignificant. There is no single universal list of stop words used by all natural language processing tools, nor any agreed upon rules for identifying stop words, and indeed not all tools even use such a list. Therefore, any group of words can be chosen as the stop words for a given purpose. The "general trend in nformation retrieval systems over time has been from standard use of quite large stop lists (200–300 terms) to very small stop lists (7–12 terms) to no stop list whatsoever". History of stop words A predecessor concept was used in creating some concordances. For example, the first Hebrew concordance, Isaac Nathan ben Kalonymus's he, Me’ir Nativ, label=none, script=latn, contained a one-page list of unindexed words, with nonsubstantive prepositions and conjunctions ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Natural Language Processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lexical Word
In grammar, a part of speech or part-of-speech (abbreviated as POS or PoS, also known as word class or grammatical category) is a category of words (or, more generally, of lexical items) that have similar grammatical properties. Words that are assigned to the same part of speech generally display similar syntactic behavior (they play similar roles within the grammatical structure of sentences), sometimes similar morphological behavior in that they undergo inflection for similar properties and even similar semantic behavior. Commonly listed English parts of speech are noun, verb, adjective, adverb, pronoun, preposition, conjunction, interjection, numeral, article, and determiner. Other terms than ''part of speech''—particularly in modern linguistic classifications, which often make more precise distinctions than the traditional scheme does—include word class, lexical class, and lexical category. Some authors restrict the term ''lexical category'' to refer only to a particu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Text Mining
Text mining, also referred to as ''text data mining'', similar to text analytics, is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a KDD (Knowledge Discovery in Databases) process. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and inte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stemming
In linguistic morphology and information retrieval, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form—generally a written word form. The stem need not be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this stem is not in itself a valid root. Algorithms for stemming have been studied in computer science since the 1960s. Many search engines treat words with the same stem as synonyms as a kind of query expansion, a process called conflation. A computer program or subroutine that stems word may be called a ''stemming program'', ''stemming algorithm'', or ''stemmer''. Examples A stemmer for English operating on the stem ''cat'' should identify such strings as ''cats'', ''catlike'', and ''catty''. A stemming algorithm might also reduce the words ''fishing'', ''fished'', and ''fisher'' to the stem ''fish''. The stem need not be a word, for examp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Query Expansion
Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. In the context of search engines, query expansion involves evaluating a user's input (what words were typed into the search query area, and sometimes other types of data) and expanding the search query to match additional documents. Query expansion involves techniques such as: * Finding synonyms of words, and searching for the synonyms as well * Finding semantically related words (e.g. antonyms, meronyms, hyponyms, hypernyms) * Finding all the various morphological forms of words by stemming each word in the search query * Fixing spelling errors and automatically searching for the corrected form or suggesting it in the results * Re-weighting the terms in the original query Query expansion is a methodology studied in the field of computer science, particularly within the realm of natural langu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Extraction
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction Due to the difficulty of the problem, current approaches to IE (as of 2010) focus on narrowly restricted domains. An example is the extraction from newswire reports of corporate mergers, such as denoted by the formal relation: :\mathrm(company_1, company_2, date), from an online news sentence such as: :''"Yesterday, New York based Foo Inc. announced their acquisition of Bar Corp."'' A broad goal of IE is to allow computation to be done on the previously unstructured data. A more sp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Index (search Engine)
Search engine indexing is the collecting, parsing, and storing of data to facilitate fast and accurate information retrieval. Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science. An alternate name for the process, in the context of search engines designed to find web pages on the Internet, is ''web indexing''. Popular engines focus on the full-text indexing of online, natural language documents. Media types such as pictures, video, audio, and graphics 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 with the corpus. Unlike full-text indices, partial-text services restrict the depth indexed to reduce index size. Larger services typically perform indexing at a predetermined time interval due to the required time and processing costs, while agent-based search engines inde ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Function Words
In linguistics, function words (also called functors) are words that have little lexical meaning or have ambiguous meaning and express grammatical relationships among other words within a sentence, or specify the attitude or mood of the speaker. They signal the structural relationships that words have to one another and are the glue that holds sentences together. Thus they form important elements in the structures of sentences. Words that are not function words are called ''content words'' (or open class words, ''lexical words,'' or ''autosemantic words'') and include nouns, most verbs, adjectives, and most adverbs although some adverbs are function words (like ''then'' and ''why''). Dictionaries define the specific meanings of content words but can describe only the general usages of function words. By contrast, grammars describe the use of function words in detail but treat lexical words only in general terms. Since it was first proposed in 1952 by C. C. Fries, the distinguish ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Filler (linguistics)
In linguistics, a filler, filled pause, hesitation marker or planner is a sound or word that participants in a conversation use to signal that they are pausing to think but are not finished speaking.Juan, Stephen (2010).Why do we say 'um', 'er', or 'ah' when we hesitate in speaking? (These are not to be confused with placeholder names, such as ''thingamajig'', ''whatchamacallit'', ''whosawhatsa'' and ''whats'isface'', which refer to objects or people whose names are temporarily forgotten, irrelevant, or unknown.) Fillers fall into the category of formulaic language, and different languages have different characteristic filler sounds. The term filler also has a separate use in the syntactic description of wh-movement constructions. Usage Every conversation involves turn-taking, which means that whenever someone wants to speak and hears a pause, they do so. Pauses are commonly used to indicate that someone's turn has ended, which can create confusion when someone has not finished ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
To Be Or Not To Be
To Be or Not to Be may refer to: * ''To be, or not to be'', the soliloquy from ''Hamlet''. Films and TV, theatre and books * ''To Be or Not to Be'' (1942 film), directed by Ernst Lubitsch * ''To Be or Not to Be'' (1983 film), a remake produced by Mel Brooks * ''To Be or Not to Be'' (TV series), starring Maggie Cheung Ho-yee and Prudence Liew * ''To Be or Not to Be'' (play), by Nick Whitby * '' To Be or Not to Be: That Is the Adventure'', an adventure book by Ryan North * "To Be or Not to Be", television series episode, see List of seaQuest DSV episodes Music Albums * ''To Be or Not to Be'' (album), a 2013 album by Nightmare *''To Be or Not to Be'', album by Cliffhanger (band) *''To Be or Not to Be'', album by Crash (South Korean band) Songs *"To Be or Not to Be", 1980 song by BA Robertson *"To Be or Not to Be (The Hitler Rap)", 1983 song by Mel Brooks *"To Be or Not to Be", Otis Leavill, composed by Billy Butler 1965 *"To Be or Not to Be", 1965 song by the Bee Gees, from ' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
