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Text, Speech And Dialogue
Text, Speech and Dialogue (TSD) is an annual conference involving topics on natural language processing and computational linguistics. The meeting is held every September alternating in Brno and Plzeň, Czech Republic. The first Text, Speech and Dialogue conference took place in Brno in 1998. Overview TSD series evolved as a prime forum for interaction between researchers in both spoken and written language processing from all over the world. Proceedings of TSD form a book published by Springer-Verlag in their Lecture Notes in Artificial Intelligence (LNAI) series. TSD proceedings are regularly indexed by Thomson Reuters Conference Proceedings Citation Index. Moreover, LNAI series are listed in all major citation databases such as DBLP, SCOPUS, EI, INSPEC or COMPENDEX. The conference is organized by the Faculty of Informatics, Masaryk University, Brno, and the Faculty of Applied Sciences, University of West Bohemia, Plzeň. The conference is supported by the International Spe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speech Recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the main benefit of searchability. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis. Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker-independent" systems. Systems that use training are called "speaker dependent". Speech recognition ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
James Pustejovsky
James Pustejovsky (born 1956) is an American computer scientist. He is the TJX Feldberg professor of computer science at Brandeis University in Waltham, Massachusetts, United States. His expertise includes theoretical and computational modeling of language, specifically: Computational linguistics, Lexical semantics, Knowledge representation, temporal and spatial reasoning and Extraction. His main topics of research are Natural language processing generally, and in particular, the computational analysis of linguistic meaning. He holds a B.S. from MIT as well as a PhD from the University of Massachusetts, Amherst. Pustejovsky first proposed generative lexicon theory in lexical semantics in an article published in 1991, which was further developed in his 1995 book, of the same name. His other interests include temporal reasoning, event semantics, spatial language, language annotation, computational linguistics, and machine learning. Current research Pustejovsky's research ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Facial Animation
Computer facial animation is primarily an area of computer graphics that encapsulates methods and techniques for generating and animating images or models of a character face. The character can be a human, a humanoid, an animal, a legendary creature or character, etc. Due to its subject and output type, it is also related to many other scientific and artistic fields from psychology to traditional animation. The importance of face, human faces in communication, verbal and non-verbal communication and advances in Graphics processing unit, computer graphics hardware and software have caused considerable scientific, technological, and artistic interests in computer facial animation. Although development of computer graphics methods for facial animation started in the early-1970s, major achievements in this field are more recent and happened since the late 1980s. The body of work around computer facial animation can be divided into two main areas: techniques to generate animation data, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Translation
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation or interactive translation), is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another. On a basic level, MT performs mechanical substitution of words in one language for words in another, but that alone rarely produces a good translation because recognition of whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalent words in another language, and many words have more than one meaning. Solving this problem with corpus statistical and neural techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies. Current machine translation software often allows for customizat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Question Answering
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language. Overview A question answering implementation, usually a computer program, may construct its answers by querying a structured database of knowledge or information, usually a knowledge base. More commonly, question answering systems can pull answers from an unstructured collection of natural language documents. Some examples of natural language document collections used for question answering systems include: * a local collection of reference texts * internal organization documents and web pages * compiled newswire reports * a set of Wikipedia pages * a subset of World Wide Web pages Types of question answering Question answering research attempts to deal with a wide range of question types including: fact, list, definition, ''H ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Natural Language Understanding
Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem. There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis. History The program STUDENT, written in 1964 by Daniel Bobrow for his PhD dissertation at MIT, is one of the earliest known attempts at natural-language understanding by a computer. Eight years after John McCarthy coined the term artificial intelligence, Bobrow's dissertation (titled ''Natural Language Input for a Computer Problem Solving System'') showed how a computer could understand simple natural language input to solve algebra word problems. A year later, in 1965, J ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Plagiarism Detection
Plagiarism detection or content similarity detection is the process of locating instances of plagiarism or copyright infringement within a work or document. The widespread use of computers and the advent of the Internet have made it easier to plagiarize the work of others.Bretag, T., & Mahmud, S. (2009). A model for determining student plagiarism: Electronic detection and academic judgement. ''Journal of University Teaching & Learning Practice, 6''(1). Retrieved from http://ro.uow.edu.au/jutlp/vol6/iss1/6 Detection of plagiarism can be undertaken in a variety of ways. Human detection is the most traditional form of identifying plagiarism from written work. This can be a lengthy and time-consuming task for the reader and can also result in inconsistencies in how plagiarism is identified within an organization. Text-matching software (TMS), which is also referred to as "plagiarism detection software" or "anti-plagiarism" software, has become widely available, in the form of both com ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sense Disambiguation
A sense is a biological system used by an organism for sensation, the process of gathering information about the world through the detection of stimuli. (For example, in the human body, the brain which is part of the central nervous system receives signals from the senses which continuously receive information from the environment, interprets these signals, and causes the body to respond, either chemically or physically.) Although traditionally five human senses were identified as such (namely sight, smell, touch, taste, and hearing), it is now recognized that there are many more. Senses used by non-human organisms are even greater in variety and number. During sensation, sense organs collect various stimuli (such as a sound or smell) for transduction, meaning transformation into a form that can be understood by the brain. Sensation and perception are fundamental to nearly every aspect of an organism's cognition, behavior and thought. In organisms, a sensory organ consists of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Representation
Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of ''reasoning'', such as the application of rules or the relations of sets and subsets. Examples of knowledge representation formalisms include semantic nets, systems architecture, frames, rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers. History The earliest work in computerized knowledge represe ... [...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] |
Speech Synthesis
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition. Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output. The quality of a speech synthesizer is judged by its similarity to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
