|
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] |
Unstructured Data
Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated ( semantically tagged) in documents. In 1998, Merrill Lynch said "unstructured data comprises the vast majority of data found in an organization, some estimates run as high as 80%." It's unclear what the source of this number is, but nonetheless it is accepted by some. Other sources have reported similar or higher percentages of unstructured data. , IDC and Dell EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from the beginning of 2010. More recently, IDC and Seagate predict that the global data ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Relational Database
A relational database is a (most commonly digital) database based on the relational model of data, as proposed by E. F. Codd in 1970. A system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems are equipped with the option of using the SQL (Structured Query Language) for querying and maintaining the database. History The term "relational database" was first defined by E. F. Codd at IBM in 1970. Codd introduced the term in his research paper "A Relational Model of Data for Large Shared Data Banks". In this paper and later papers, he defined what he meant by "relational". One well-known definition of what constitutes a relational database system is composed of Codd's 12 rules. However, no commercial implementations of the relational model conform to all of Codd's rules, so the term has gradually come to describe a broader class of database systems, which at a minimum: # Present the data to the user as re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Text Corpus
In linguistics, a corpus (plural ''corpora'') or text corpus is a language resource consisting of a large and structured set of texts (nowadays usually electronically stored and processed). In corpus linguistics, they are used to do statistical analysis and hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory. In search technology, a corpus is the collection of documents which is being searched. Overview A corpus may contain texts in a single language (''monolingual corpus'') or text data in multiple languages (''multilingual corpus''). In order to make the corpora more useful for doing linguistic research, they are often subjected to a process known as annotation. An example of annotating a corpus is part-of-speech tagging, or ''POS-tagging'', in which information about each word's part of speech (verb, noun, adjective, etc.) is added to the corpus in the form of ''tags''. Another example is indicating the lemma (ba ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Terminology Extraction
Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc. The development of terminology extraction is also essential to the language industry. One of the first steps to model a knowledge domain is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain concepts. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature. Typically, approaches to aut ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Relationship Extraction
A relationship extraction task requires the detection and classification of semantic relationship mentions within a set of artifacts, typically from text or XML documents. The task is very similar to that of information extraction (IE), but IE additionally requires the removal of repeated relations ( disambiguation) and generally refers to the extraction of many different relationships. Concept and applications The concept of relationship extraction was first introduced during the 7th Message Understanding Conference in 1998. Relationship extraction involves the identification of relations between entities and it usually focuses on the extraction of binary relations. Application domains where relationship extraction is useful include gene-disease relationships, protein-protein interaction etc. Current relationship extraction studies use machine learning technologies, which approach relationship extraction as a classification problem. Never-Ending Language Learning is a semantic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Anaphora (linguistics)
In linguistics, anaphora () is the use of an expression whose interpretation depends upon another expression in context (its antecedent or postcedent). In a narrower sense, anaphora is the use of an expression that depends specifically upon an antecedent expression and thus is contrasted with cataphora, which is the use of an expression that depends upon a postcedent expression. The anaphoric (referring) term is called an anaphor. For example, in the sentence ''Sally arrived, but nobody saw her'', the pronoun ''her'' is an anaphor, referring back to the antecedent ''Sally''. In the sentence ''Before her arrival, nobody saw Sally'', the pronoun ''her'' refers forward to the postcedent ''Sally'', so ''her'' is now a ''cataphor'' (and an anaphor in the broader, but not the narrower, sense). Usually, an anaphoric expression is a pro-form or some other kind of deictic (contextually dependent) expression. Both anaphora and cataphora are species of endophora, referring to something m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Coreference
In linguistics, coreference, sometimes written co-reference, occurs when two or more expressions refer to the same person or thing; they have the same referent. For example, in ''Bill said Alice would arrive soon, and she did'', the words ''Alice'' and ''she'' refer to the same person. Co-reference is often non-trivial to determine. For example, in ''Bill said he would come'', the word ''he'' may or may not refer to Bill. Determining which expressions are coreferences is an important part of analyzing or understanding the meaning, and often requires information from the context, real-world knowledge, such as tendencies of some names to be associated with particular species ("Rover"), kinds of artifacts ("Titanic"), grammatical genders, or other properties. Linguists commonly use indices to notate coreference, as in ''Billi said hei would come''. Such expressions are said to be ''coindexed'', indicating that they should be interpreted as coreferential. When expressions are corefer ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Coreference
In linguistics, coreference, sometimes written co-reference, occurs when two or more expressions refer to the same person or thing; they have the same referent. For example, in ''Bill said Alice would arrive soon, and she did'', the words ''Alice'' and ''she'' refer to the same person. Co-reference is often non-trivial to determine. For example, in ''Bill said he would come'', the word ''he'' may or may not refer to Bill. Determining which expressions are coreferences is an important part of analyzing or understanding the meaning, and often requires information from the context, real-world knowledge, such as tendencies of some names to be associated with particular species ("Rover"), kinds of artifacts ("Titanic"), grammatical genders, or other properties. Linguists commonly use indices to notate coreference, as in ''Billi said hei would come''. Such expressions are said to be ''coindexed'', indicating that they should be interpreted as coreferential. When expressions are corefer ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Named Entity Recognition
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. Most research on NER/NEE systems has been structured as taking an unannotated block of text, such as this one: And producing an annotated block of text that highlights the names of entities: In this example, a person name consisting of one token, a two-token company name and a temporal expression have been detected and classified. State-of-the-art NER systems for English produce near-human performance. For example, the best system entering MUC-7 scored 93.39% of F-measure while human annotators scored 97.60% and 96.95%. Named-entity recognition platforms Notable NER platforms includ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Michelle Obama
Michelle LaVaughn Robinson Obama (born January 17, 1964) is an American attorney and author who served as first lady of the United States from 2009 to 2017. She was the first African-American woman to serve in this position. She is married to former President Barack Obama. Raised on the South Side of Chicago, Obama is a graduate of Princeton University and Harvard Law School. In her early legal career, she worked at the law firm Sidley Austin where she met Barack Obama. She subsequently worked in nonprofits and as the associate dean of Student Services at the University of Chicago as well as the vice president for Community and External Affairs of the University of Chicago Medical Center. Michelle married Barack in 1992, and together they have two daughters. Obama campaigned for her husband's presidential bid throughout 2007 and 2008, delivering a keynote address at the 2008 Democratic National Convention. She has subsequently delivered acclaimed speeches at the 2012, 2 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Barack Obama
Barack Hussein Obama II ( ; born August 4, 1961) is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party (United States), Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and previously worked as a civil rights lawyer before entering politics. Obama was born in Honolulu, Hawaii. After graduating from Columbia University in 1983, he worked as a Community organizing, community organizer in Chicago. In 1988, he enrolled in Harvard Law School, where he was the first black president of the ''Harvard Law Review''. After graduating, he became a civil rights attorney and an academic, teaching constitutional law at the University of Chicago Law School from 1992 to 2004. Turning to elective politics, he Illinois Senate career of Barack Obama, repre ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Base
A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems. Original usage of the term The original use of the term knowledge base was to describe one of the two sub-systems of an expert system. A knowledge-based system consists of a knowledge-base representing facts about the world and ways of reasoning about those facts to deduce new facts or highlight inconsistencies. Properties The term "knowledge-base" was coined to distinguish this form of knowledge store from the more common and widely used term '' database''. During the 1970s, virtually all large management information systems stored their data in some type of hierarchical or relational database. At this point in the history of information technology, the distinction between a database and a knowledge-base was clear and unambiguou ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
