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Document Clustering
Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering. Overview Document clustering involves the use of descriptors and descriptor extraction. Descriptors are sets of words that describe the contents within the cluster. Document clustering is generally considered to be a centralized process. Examples of document clustering include web document clustering for search users. The application of document clustering can be categorized to two types, online and offline. Online applications are usually constrained by efficiency problems when compared to offline applications. Text clustering may be used for different tasks, such as grouping similar documents (news, tweets, etc.) and the analysis of customer/employee feedback, discovering meaningful implicit subjects across all documents. In general, there are two common algor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cluster Analysis
Cluster analysis or clustering is the data analyzing technique in which task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more Similarity measure, similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistics, statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Cluster analysis refers to a family of algorithms and tasks rather than one specific algorithm. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small Distance function, distances between cluster members, dense areas of the data space, intervals or pa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bag-of-words Model
The bag-of-words (BoW) model is a model of text which uses an unordered collection (a "multiset, bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures Multiplicity (mathematics), multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a Feature (machine learning), feature for training a Statistical classification, classifier. It has also been Bag-of-words model in computer vision, used for computer vision. An early reference to "bag of words" in a linguistic context can be found in Zellig Harris's 1954 article on ''Distributional Structure''. Definition The following models a text document using bag-of-words. Here are two simple text documents: (1) John likes to watch movies. Mary likes movies too. (2) Mary also likes to watch football games. Based on ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cluster (other)
may refer to: Science and technology Astronomy * Cluster (spacecraft), constellation of four European Space Agency spacecraft * Cluster II (spacecraft), a European Space Agency mission to study the magnetosphere * Asteroid cluster, a small asteroid family * Galaxy cluster, large gravitationally bound groups of galaxies, or groups of groups of galaxies * Supercluster, the largest gravitationally bound objects in the universe, composed of many galaxy clusters * Star cluster ** Globular cluster, a spherical collection of stars whose orbit is either partially or completely in the halo of the parent galaxy ** Open cluster, a spherical collection of stars that orbits a galaxy in the galactic plane Biology and medicine * Cancer cluster, in biomedicine, an occurrence of a greater-than-expected number of cancer cases * Cluster headache, a neurological disease that involves an immense degree of pain * Cluster of differentiation, protocol used for the identification and investigati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Supervised Learning
In machine learning, supervised learning (SL) is a paradigm where a Statistical model, model is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a ''supervisory signal''), which are often human-made labels. The training process builds a function that maps new data to expected output values. An optimal scenario will allow for the algorithm to accurately determine output values for unseen instances. This requires the learning algorithm to Generalization (learning), generalize from the training data to unseen situations in a reasonable way (see inductive bias). This statistical quality of an algorithm is measured via a ''generalization error''. Steps to follow To solve a given problem of supervised learning, the following steps must be performed: # Determine the type of training samples. Before doing anything else, the user should decide what kind of data is to be used as a Training, validation, and test data sets, trainin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multidimensional Scaling
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a data set. MDS is used to translate distances between each pair of n objects in a set into a configuration of n points mapped into an abstract Cartesian space. More technically, MDS refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix. It is a form of non-linear dimensionality reduction. Given a distance matrix with the distances between each pair of objects in a set, and a chosen number of dimensions, ''N'', an MDS algorithm places each object into ''N''-dimensional space (a lower-dimensional representation) such that the between-object distances are preserved as well as possible. For ''N'' = 1, 2, and 3, the resulting points can be visualized on a scatter plot. Core theoretical contributions to MDS were made by James O. Ramsay of McGill University, who is also ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Punctuation
Punctuation marks are marks indicating how a piece of writing, written text should be read (silently or aloud) and, consequently, understood. The oldest known examples of punctuation marks were found in the Mesha Stele from the 9th century BC, consisting of points between the words and horizontal strokes between sections. The alphabet-based writing began with no spaces, no capitalization, no vowels (see abjad), and with only a few punctuation marks, as it was mostly aimed at recording business transactions. Only with the Greek playwrights (such as Euripides and Aristophanes) did the ends of sentences begin to be marked to help actors know when to make a pause during performances. Punctuation includes Space (punctuation), space between words and both obsolete and modern signs. By the 19th century, the punctuation marks were used hierarchically, according to their weight. Six marks, proposed in 1966 by the French author Hervé Bazin, could be seen as predecessors of emoticons and e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stop Words
Stop words are the words in a stop list (or ''stoplist'' or ''negative dictionary'') which are filtered out ("stopped") before or after processing of natural language data (i.e. text) because they are deemed to have little semantic value or are otherwise insignificant for the task at hand. There is no single universal list of stop words used by all natural language processing (NLP) 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 retrievalsystems 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 , contained a one-page list of unindexed words, with nonsu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lemmatization
Lemmatization (or less commonly lemmatisation) in linguistics is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form. In computational linguistics, lemmatization is the algorithmic process of determining the lemma of a word based on its intended meaning. Unlike stemming, lemmatization depends on correctly identifying the intended part of speech and meaning of a word in a sentence, as well as within the larger context surrounding that sentence, such as neighbouring sentences or even an entire document. As a result, developing efficient lemmatization algorithms is an open area of research. Description In many languages, words appear in several ''inflected'' forms. For example, in English, the verb 'to walk' may appear as 'walk', 'walked', 'walks' or 'walking'. The base form, 'walk', that one might look up in a dictionary, is called the ''lemma'' for the word. The association of ... [...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 example ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
N-gram Model
A word ''n''-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words. If only one previous word is considered, it is called a bigram model; if two words, a trigram model; if ''n'' − 1 words, an ''n''-gram model. Special tokens are introduced to denote the start and end of a sentence \langle s\rangle and \langle /s\rangle. To prevent a zero probability being assigned to unseen words, each word's probability is slightly higher than its frequency count in a corpus. To calculate it, various methods were used, from simple "add-one" smoothing (assign a count of 1 to unseen ''n''-grams, as an uninformative prior) to more sophisticated models, such as Good–Turing discounting or back-off models. Unigram model A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Tokenization (lexical Analysis)
Lexical tokenization is conversion of a text into (semantically or syntactically) meaningful ''lexical tokens'' belonging to categories defined by a "lexer" program. In case of a natural language, those categories include nouns, verbs, adjectives, punctuations etc. In case of a programming language, the categories include identifiers, operators, grouping symbols, data types and language keywords. Lexical tokenization is related to the type of tokenization used in large language models (LLMs) but with two differences. First, lexical tokenization is usually based on a lexical grammar, whereas LLM tokenizers are usually probability-based. Second, LLM tokenizers perform a second step that converts the tokens into numerical values. Rule-based programs A rule-based program, performing lexical tokenization, is called ''tokenizer'', or ''scanner'', although ''scanner'' is also a term for the first stage of a lexer. A lexer forms the first phase of a compiler frontend in processing. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
