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Text Graph
In natural language processing (NLP), a text graph is a graph representation of a text item (document, passage or sentence). It is typically created as a preprocessing step to support NLP tasks such as text condensation term disambiguation (topic-based) text summarization, relation extraction and textual entailment. Representation The semantics of what a text graph's nodes and edges represent can vary widely. Nodes for example can simply connect to tokenized words, or to domain-specific terms, or to entities mentioned in the text. The edges, on the other hand, can be between these text-based tokens or they can also link to a knowledge base. TextGraphs Workshop series The TextGraphs Workshop series{{cite web, url=http://www.textgraphs.org/, title=Textgraphs, access-date=6 March 2017 is a series of regular academic workshops intended to encourage the synergy between the fields of natural language processing (NLP) and graph theory. The mix between the two started small, with gra ... [...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] |
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] |
Graph Database
A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. A key concept of the system is the ''graph'' (or ''edge'' or ''relationship''). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data. Graph databases are commonly referred to as a NoSQL. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction and lac ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hyperlinking
In computing, a hyperlink, or simply a link, is a digital reference to data that the user can follow or be guided by clicking or tapping. A hyperlink points to a whole document or to a specific element within a document. Hypertext is text with hyperlinks. The text that is linked from is known as anchor text. A software system that is used for viewing and creating hypertext is a ''hypertext system'', and to create a hyperlink is ''to hyperlink'' (or simply ''to link''). A user following hyperlinks is said to ''navigate'' or ''browse'' the hypertext. The document containing a hyperlink is known as its source document. For example, in an online reference work such as Wikipedia or Google, many words and terms in the text are hyperlinked to definitions of those terms. Hyperlinks are often used to implement reference mechanisms such as tables of contents, footnotes, bibliographies, indexes, letters, and glossaries. In some hypertext, hyperlinks can be bidirectional: they can be f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Document-term Matrix
A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in a collection of documents. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix is a specific instance of a document-feature matrix where "features" may refer to other properties of a document besides terms. It is also common to encounter the transpose, or term-document matrix where documents are the columns and terms are the rows. They are useful in the field of natural language processing and computational text analysis. While the value of the cells is commonly the raw count of a given term, there are various schemes for weighting the raw counts such as, row normalizing (i.e. relative frequency/proportions) and tf-idf. Terms are commonly single words separated by whitespace or punctuation on either side (a.k.a. unigrams). In such a case, this is also referred to as "bag of words" representation because the counts ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Document Classification
Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science. The problems are overlapping, however, and there is therefore interdisciplinary research on document classification. The documents to be classified may be texts, images, music, etc. Each kind of document possesses its special classification problems. When not otherwise specified, text classification is implied. Documents may be classified according to their subjects or according to other attributes (such as document type, author, printing year etc.). In the rest of this article only subject classification is considered. T ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bag-of-words Model
The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model has also been used for computer vision. The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier. An early reference to "bag of words" in a linguistic context can be found in Zellig Harris's 1954 article on ''Distributional Structure''. The Bag-of-words model is one example of a Vector space model. Example implementation 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 these two text d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Canada
Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over , making it the world's second-largest country by total area. Its southern and western border with the United States, stretching , is the world's longest binational land border. Canada's capital is Ottawa, and its three largest metropolitan areas are Toronto, Montreal, and Vancouver. Indigenous peoples have continuously inhabited what is now Canada for thousands of years. Beginning in the 16th century, British and French expeditions explored and later settled along the Atlantic coast. As a consequence of various armed conflicts, France ceded nearly all of its colonies in North America in 1763. In 1867, with the union of three British North American colonies through Confederation, Canada was formed as a federal dominion of four provinces. This began an accretion of provinces an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
British Columbia
British Columbia (commonly abbreviated as BC) is the westernmost province of Canada, situated between the Pacific Ocean and the Rocky Mountains. It has a diverse geography, with rugged landscapes that include rocky coastlines, sandy beaches, forests, lakes, mountains, inland deserts and grassy plains, and borders the province of Alberta to the east and the Yukon and Northwest Territories to the north. With an estimated population of 5.3million as of 2022, it is Canada's third-most populous province. The capital of British Columbia is Victoria and its largest city is Vancouver. Vancouver is the third-largest metropolitan area in Canada; the 2021 census recorded 2.6million people in Metro Vancouver. The first known human inhabitants of the area settled in British Columbia at least 10,000 years ago. Such groups include the Coast Salish, Tsilhqotʼin, and Haida peoples, among many others. One of the earliest British settlements in the area was Fort Victoria, established ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Vancouver
Vancouver ( ) is a major city in western Canada, located in the Lower Mainland region of British Columbia. As the List of cities in British Columbia, most populous city in the province, the 2021 Canadian census recorded 662,248 people in the city, up from 631,486 in 2016. The Greater Vancouver, Greater Vancouver area had a population of 2.6million in 2021, making it the List of census metropolitan areas and agglomerations in Canada#List, third-largest metropolitan area in Canada. Greater Vancouver, along with the Fraser Valley Regional District, Fraser Valley, comprises the Lower Mainland with a regional population of over 3 million. Vancouver has the highest population density in Canada, with over 5,700 people per square kilometre, and fourth highest in North America (after New York City, San Francisco, and Mexico City). Vancouver is one of the most Ethnic origins of people in Canada, ethnically and Languages of Canada, linguistically diverse cities in Canada: 49.3 percent of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Association For Computational Linguistics
The Association for Computational Linguistics (ACL) is a scientific and professional organization for people working on natural language processing. Its namesake conference is one of the primary high impact conferences for natural language processing research, along with EMNLP. The conference is held each summer in locations where significant computational linguistics research is carried out. It was founded in 1962, originally named the Association for Machine Translation and Computational Linguistics (AMTCL). It became the ACL in 1968. The ACL has a European ( EACL), a North American (NAACL), and an Asian (AACL) chapter. History The ACL was founded in 1962 as the Association for Machine Translation and Computational Linguistics (AMTCL). The initial membership was about 100. In 1965 the AMTCL took over the journal ''Mechanical Translation and Computational Linguistics''. This journal was succeeded by many other journals: ''American Journal of Computational Linguistics'' (1974— ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology Learning
Ontology learning (ontology extraction, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontology (information science), ontologies, including extracting the corresponding Domain of discourse, domain's terms and the relationships between the Conceptualization (information science), concepts that these terms represent from a Text corpus, corpus of natural language text, and encoding them with an ontology language for easy retrieval. As ontology engineering, building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process. Typically, the process starts by terminology extraction, extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical or symbolic techniques are used to extract relation signatures, often based on pattern-based or definition-based hypernym extraction techniques. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
