Notes:
A language understanding engine is a software system or algorithm that is designed to understand the underlying meaning of text. It is a type of natural language processing (NLP) technology that is used to analyze and interpret text, and can be used to identify the intentions, motivations, and meanings of the text.
Language understanding engines can be used to improve the accuracy and efficiency of various tasks that involve the processing of natural language, such as information retrieval, machine translation, and dialog systems. They can be trained on large datasets of annotated text to learn the patterns and structures of language, and can then be used to analyze and interpret new texts based on this knowledge.
Speech recognition is another type of language “understanding” technology, as it involves the process of converting spoken language into text. Speech recognition systems use algorithms and machine learning techniques to analyze and interpret spoken language, and can be used to transcribe spoken words into written text or to understand and respond to spoken commands. Like language understanding engines, speech recognition systems can be trained on large datasets of annotated speech to learn the patterns and structures of language, and can then be used to recognize and interpret new speech based on this knowledge.
- Boeing Language Understanding Engine (BLUE) is a natural language processing (NLP) system developed by Boeing to understand and interpret spoken and written language. BLUE is designed to improve the accuracy and efficiency of various tasks that involve the processing of natural language, such as information retrieval, machine translation, and dialog systems. It uses machine learning techniques to analyze and interpret language, and can be trained on large datasets of annotated text to learn the patterns and structures of language.
- Computational linguistics engine is a software system or algorithm that is designed to analyze and interpret natural language using computational techniques and theories of linguistics. It is a type of natural language processing (NLP) technology that is used to improve the accuracy and efficiency of various tasks that involve the processing of natural language, such as information retrieval, machine translation, and dialog systems. Computational linguistics engines can be trained on large datasets of annotated text to learn the patterns and structures of language, and can then be used to analyze and interpret new texts based on this knowledge.
- Linguistics engine is a software system or algorithm that is designed to analyze and interpret natural language using theories and techniques from the field of linguistics. It is a type of natural language processing (NLP) technology that is used to improve the accuracy and efficiency of various tasks that involve the processing of natural language, such as information retrieval, machine translation, and dialog systems. Linguistics engines can be trained on large datasets of annotated text to learn the patterns and structures of language, and can then be used to analyze and interpret new texts based on this knowledge.
- Natural language understanding engine is a software system or algorithm that is designed to understand and interpret natural language. It is a type of natural language processing (NLP) technology that is used to analyze and interpret text or speech, and can be used to identify the intentions, motivations, and meanings of the language. Natural language understanding engines can be used to improve the accuracy and efficiency of various tasks that involve the processing of natural language, such as information retrieval, machine translation, and dialog systems.
- Speech recognition engine is a software system or algorithm that is designed to convert spoken language into text. It is a type of natural language processing (NLP) technology that is used to analyze and interpret spoken language, and can be used to transcribe spoken words into written text or to understand and respond to spoken commands. Speech recognition engines use machine learning techniques to analyze and interpret spoken language, and can be trained on large datasets of annotated speech to learn the patterns and structures of language.
- Spoken language understanding engine is a software system or algorithm that is designed to understand and interpret spoken language. It is a type of natural language processing (NLP) technology that is used to analyze and interpret spoken language, and can be used to identify the intentions, motivations, and meanings of the language. Spoken language understanding engines can be used to improve the accuracy and efficiency of various tasks that involve the processing of spoken language, such as information retrieval, machine translation, and dialog systems. Like speech recognition engines, spoken language understanding engines use machine learning techniques to analyze and interpret spoken language, and can be trained on large datasets of annotated speech to learn the patterns and structures of language.
Wikipedia:
See also:
100 Best Natural Language Understanding Videos | Language Understanding Module | Natural Language Understanding | Scene Understanding & Natural Language 2016 | Story Understanding Systems | VUE (Visual Understanding Environment)
Web-style ranking and SLU combination for dialog state tracking
JD Williams – Proceedings of the 15th Annual Meeting of the Special …, 2014 – aclweb.org
… The second contribution is to incorporate the output of multiple spoken language understanding engines (SLUs) into dialog state tracking. Using more than one SLU can increase the number of di- alog states being tracked, improving the chances of discovering the correct one …
Augmenting WordNet for deep understanding of text
P Clark, C Fellbaum, JR Hobbs, P Harrison… – Proceedings of the …, 2008 – dl.acm.org
… It is yet to be seen whether very high performance in RTE can be obtained without some kind of deep language understand- ing of the entire scene that a text conveys. We are testing our work with BLUE, Boeing’s Language Understanding Engine, which we first describe …
An Inference-Based Approach to Recognizing Entailment.
P Clark, P Harrison – TAC, 2009 – pdfs.semanticscholar.org
… For this year’s RTE challenge we have con- tinued to pursue a (somewhat) “logical” approach to recognizing entailment, in which our system, called BLUE (Boeing Language Understanding Engine) first cre- ates a logic-based representation of a text T and then performs simple …
Naturalness vs. predictability: A key debate in controlled languages
P Clark, WR Murray, P Harrison… – International Workshop on …, 2009 – Springer
Page 1. NE Fuchs (Ed.): CNL 2009 Workshop, LNAI 5972, pp. 65–81, 2010. © Springer-Verlag Berlin Heidelberg 2010 Naturalness vs. Predictability: A Key Debate in Controlled Languages Peter Clark, William R. Murray, Phil Harrison, and John Thompson …
Efficient algorithm for rational kernel evaluation in large lattice sets
J Švec, P Ircing – … and Signal Processing (ICASSP), 2013 IEEE …, 2013 – ieeexplore.ieee.org
… First, the speech and language processing tools are often designed in a “cascade” fash- ion where the (generally ambiguous) output of one module – eg speech recognizer – is passed into the next one, for example natural language understanding engine …
Boeing’s NLP system and the challenges of semantic representation
P Clark, P Harrison – Proceedings of the 2008 Conference on Semantics …, 2008 – dl.acm.org
… 1.1 Overview and Scope As our contribution to the 2008 STEP Symposium’s “shared task” of comparing se- mantic representations (Bos, 2008), we describe Boeing’s NLP system, BLUE (Boe- ing Language Understanding Engine), and subsequently analyze its performance on …
A Study of Machine Reading from Multiple Texts.
P Clark, JA Thompson – AAAI Spring Symposium: Learning by Reading and …, 2009 – aaai.org
… We first ran each text through Boeing’s Language Understanding Engine (BLUE) (Clark and Harrison, 2008), and then analyzed issues which arose in the individual interpretations, and what it would take to then integrate those interpretations together …
A statistics-based semantic textual entailment system
P Pakray, U Barman, S Bandyopadhyay… – … Conference on Artificial …, 2011 – Springer
… and shallow semantics. The Boeing Language Understanding Engine [21] can be viewed as comprising of three main elements: parsing, WordNet and DIRT, built on top of a simple baseline of bag-of-words comparison. A joint …
An intelligent call center platform for mobile communication enterprises
W Wang, L Liu, D Wang, Y Cao, Y Wu… – Communications …, 2010 – ieeexplore.ieee.org
… Then mobile customers are allowed to send Chinese queries, through either Internet tools (eg MSN, QQ, BBS and Fetion) or Wireless SM devices (eg mobile phones), to a multi-tier natural language understanding engine, and the engine retrieves the knowledge bases to find …
Language understanding by reference resolution in episodic memory
KM Livingston – 2009 – search.proquest.com
Language understanding by reference resolution in episodic memory. Abstract. This dissertation presents an approach to language understanding that treats all ambiguity resolution as a problem of reference resolution: grounding references to episodic memory …
Fundamentals of speaker recognition
H Beigi – 2011 – books.google.com
Page 1. Homa)00m Belgi Fundamental: of Speaker Recognition © Springer Page 2. Fundamentals of Speaker Recognition Page 3. Page 4. Homayoon Beigi Fundamentals of Speaker Recognition Page 5. Homayoon Beigi Recognition Technologies, Inc …
Topic Modeling for Natural Language Understanding
X Song – 2016 – search.proquest.com
Topic Modeling for Natural Language Understanding. Abstract. This thesis presents new topic modeling methods to reveal underlying language structures. Topic models have seen many successes in natural language understanding field …
Dealing with quantifier scope ambiguity in natural language understanding
MH Manshadi – 2014 – search.proquest.com
… notion of Learning by Reading. 2.2.1 Boeing NLP System (BLUE). BLUE (Boeing Language Understanding Engine) (Clark and Harrison, 2008) transforms every sentence to a subset of rst order logic. It uses SAPIR, a classic …
A Proposal for Processing and Fusioning Multiple Information Sources in Multimodal Dialog Systems
D Griol, JM Molina, J García-Herrero – … of Agents and Multi-Agent Systems, 2014 – Springer
… W3C Multimodal Interaction Framework (www.w3.org/TR/mmi-framework/) and in- tended for use by systems that provide semantic interpretations for a variety of inputs, including speech recognition, handwriting recognizers, natural language understanding engines, and other …
Improving Part-of-Speech Tagging for NLP Pipelines
V Jatav, R Teja, S Bharadwaj, V Srinivasan – arXiv preprint arXiv …, 2017 – arxiv.org
… flexibility. One of the 20-engines is the Computational Linguistics engine that is implemented as a deep-linguistics driven Natural Language Understanding engine. The linguistic rule-driven POS Tagger is part of the NLU pipeline …
Grounding Natural Language Instructions in Industrial Robotics
D Evangelista, WU Villa, M Imperoli, A Vanzo, L Iocchi… – pdfs.semanticscholar.org
… In order to achieve the most natural and effective interaction between human and robot, we implemented a language understanding engine explicitly designed and trained for robots, and a knowledge representation architecture based on semantic maps …
Learning User Intentions in Natural Language Call Routing Systems
K Aida-zade, S Rustamov – Recent Developments and New Direction in …, 2016 – Springer
… One or a few sentences from the output of speech recognizer are taken as the user request. The language understanding engine then uses different topic identification technologies to determine the reason for the call from the sequence of recognized words …
The Future of AI
N Pathak – Artificial Intelligence for. NET: Speech, Language, and …, 2017 – Springer
… This data can come from research, legal cases, and medical diagnosis, to name a few. The natural language understanding engine can help you manipulate the text but one of the core issues is understanding the right context …
Why is it Hard to Understand Original English Questions?
P Clark, J Thompson, BP Works – 2009 – cs.utexas.edu
… insurmountable. Our full language processor (BLUE – Boeing Language Understanding Engine, (Clark and Harrison, 2008)) often identifies the correct syntactic structure for the original English sentences that the Cyc report examined …
Multimodality and Spoken Dialogue Systems
S Johar – Emotion, Affect and Personality in Speech, 2016 – Springer
… The user’s input is translated into machine readable form by an automatic speech recognizer (ASR). The recognized words are sent to a language understanding engine which interprets the semantic meaning of the input. The …
Evaluation of the New Paraphrase and Question Formulation Capability in AURA
P Clark – 2010 – cs.utexas.edu
… In addition, we also explored removing the controlled language restrictions of CPL and using the full Boeing Language Understanding Engine (BLUE), to identify its potential utility in improving AURA’s question interpretation abilities …
Speech Driven Interaction in Mobile Multimodality
G Frattini, F Corvino, F Gaudino… – Multimodal Human …, 2009 – igi-global.com
… improving the recognition rate of speech recognition engine. In other words, knowing what the user is going to be talking about makes the task of the natural language understanding engine easier. Indeed this approach is not ideal to …
Integrating and extending BIRT
J Weathersby, T Bondur, I Chatalbasheva – 2011 – books.google.com
Page 1. the eclipse series Integrating and Extending BIRT Third Edition Jason Weathersby – Tom Bonclur – lana Chatalbasheva Selies Editors JeIiMcAtfe: – siu=c».1§in., – :@m~ ‘ i , _ , =_L= _._ Page 2. Integrating and Extending BIRT Third Edition Page 3 …
Automatic Evaluation Of Computer Science thesis Using Domain Ontology
MM Hussain, SK Srivatsa – pdfs.semanticscholar.org
… 8 Logical Inference [11] Presented a RTE system that works by using logical inference. First, the authors used a system called BLUE (Boeing Language Understanding Engine) to perform a full semantic interpretation of both sentences …
Online multimodal interaction for speech interpretation
V Ingle, A Deshpande – International Journal of Computer Applications, 2010 – Citeseer
… Components that generate EMMA markup: 1. Speech recognizers 2. Handwriting recognizers 3. Natural language understanding engines 4. Other input media interpreters (eg DTMF, pointing, keyboard) 5. Multimodal integration component Components that use EMMA include …
Emotion, Affect and Personality in Speech: The Bias of Language and Paralanguage
S Johar – 2015 – books.google.com
Page 1. SPRINGER BRIEFS IN ELECTRICAL AND COMPUTER ENGINEERING ? SPEECH TECHNOLOGY Swati Johar Emotion, Affect and Personality in Speech The Bias of Language and Paralanguage 123 Page 2. SpringerBriefs …
Impact of hosted speech technology for health care service providers through call centers
R Manoharan, R Ganesan, K Sabarinathan – Sch. Res. J. Interdiscip. Stud, 2015 – oaji.net
… JAN – FEBRUARY, 2015. VOL-III/XVI www.srjis.com Page 2720 inbound IVR? Or using a natural language understanding engine to drive SMS auto-response and Web or mobile-based FAQ apps? It’s all possible because the Nuance On Demand …
SK-languages as a powerful and flexible semantic formalism for the systems of cross-lingual intelligent information access
VA Fomichov – Informatica, 2017 – search.proquest.com
… Let’s consider only one example. The linguistic processor BLUE (= Boeing Language Understanding Engine) was developed as an advanced information processing tool for the Boeing company. The system is able to build SRs of sentences of many kinds …
Research Areas
MS Baba, R Zainuddin – researchgate.net
… Fariza Hanum. Md Nasaruddin • Development of Repository for Cardiac Data and Models Kasturi Dewi a/p Varathan • Natural Language Understanding Engine for Question Answering Kiran Kaur (On Study leave) • Exploring …
Artificial conversations for customer service chatter bots: Architecture, algorithms, and evaluation metrics
C Chakrabarti, GF Luger – Expert Systems with Applications, 2015 – Elsevier
… at MIT (Seneff et al., 1998; Polifroni & Seneff, 2000) is a client–server architecture for communicating online information including weather and flight information, and consists of database access, a speech synthesizer, a speech recognizer, and a language understanding …
Sentic computing: a common-sense-based framework for concept-level sentiment analysis
E Cambria, A Hussain – 2015 – books.google.com
… truly ready back then for “in the wild” processing. Thus, the thought came to mind to train our one-pass top-down natural language understanding engine to recognize emotion from speech instead. In doing so, I was left with two …
Recognizing and Justifying Text Entailment through Distributional Navigation on Definition Graphs
VS Silva, A Freitas, S Handschuh – 2018 – andrefreitas.org
… A few exceptions in- clude the Boeing Language Understanding Engine (BLUE) (Clark and Harrison 2009), which can show evidence of why an entailment was achieved, but doesn’t provide a fully in- terpretable natural language explanation …
A pragmatic approach to computational narrative understanding
ER Tomai – 2009 – search.proquest.com
A pragmatic approach to computational narrative understanding. Abstract. Narrative understanding is a hard problem for artificial intelligence that requires deep semantic understanding of natural language and broad world knowledge …
Towards resistance detection in health behavior change dialogue systems
B Sarma – 2015 – search.proquest.com
… The resistance detection module will be interacting with the language understanding module and the dialogue manager. It will take the output from the language understanding engine and the output class will be provided as input to the dialogue manager …
Lotus Development Corp. One Rogers St. Cambridge, MA 02142 USA
BACI AGENT – … and Communication: Proceedings of the Fourth …, 2013 – books.google.com
… First, Communication between user and agent does not use full natural language, but rather an artificial language (cf Sidner [1994]); we did not want to undertake all the complexities of natural language understanding engines as part of this project …
Recognizing textual entailment
M Sammons, V Vydiswaran, D Roth – Multilingual Natural Language …, 2011 – l2r.cs.uiuc.edu
Page 1. Chapter 1 Recognizing Textual Entailment Mark Sammons, VGVinod Vydiswaran, Dan Roth University of Illinois Urbana, IL 1.1 Introduction Since 2005, researchers have worked on a broad task called Recognizing …
Different Models and Approaches of Textual Entailment Recognition
MH Haggag, MMA ELFattah… – International Journal of …, 2016 – search.proquest.com
Different Models and Approaches of Textual Entailment Recognition. Abstract. Variability of semantic expression is a fundamental phenomenon of a natural language where same meaning can be expressed by different texts …
Study on Optimal Spoken Dialogue System for Robust Information Search in the Real World
?? – 2016 – eprints.lib.hokudai.ac.jp
Page 1. Instructions for use Title Study on Optimal Spoken Dialogue System for Robust Information Search in the Real World Author(s) ?, ? Issue Date 2016-09-26 DOI 10.14943/doctoral.k12405 Doc URL http://hdl.handle.net/2115/63374 Type theses (doctoral) …
Answering questions about archived, annotated meetings
M Ailomaa – 2009 – infoscience.epfl.ch
… questions. To meet the outlined objectives, we have annotated meetings with argumentative structure and built a prototype of a natural language understanding engine that in- terprets questions based on those annotations …
Context Aware Textual Entailment
S Arab-Khazaeli – 2015 – digitalcommons.lsu.edu
… 2003). Using a statistical method, they applied the semantic features, along with other lexical and syntactical features. The Boeing Language Understanding Engine (BLUE) system by Clark and Harrison (2009) is based on a formal logical approach …
VoiceBrowse: The Dynamic Generation Of Spoken Dialogue from Online Content
C Wootton – 2008 – paulmckevitt.com
… Language Understanding Engine: Software that accepts text as input and infers the underlying meaning to the utterance. Page 13. xiii … recognised words have to be parsed and understood by the system, which is the task of the language understanding engine …
Predication Driven Textual Entailment
MA Moruz – 2011 – profs.info.uaic.ro
Page 1. “Al. I. Cuza” University of Iasi Faculty of Computer Science Predication Driven Textual Entailment 2011 PhD Student Mihai-Alex Moruz Supervisor Prof. Dr. Dan Cristea Thesis version updated and modified according to the recommendations of the reviewers. Page 2. i …
Component-Based Textual Entailment: a Modular and Linguistically-Motivated Framework for Semantic Inferences
E Cabrio – 2011 – eprints-phd.biblio.unitn.it
… 5 Page 22. 1.5. STRUCTURE OF THE THESIS CHAPTER 1. INTRODUCTION BLUE (Boeing Language Understanding Engine), and we discovered that, although the three systems have similar accuracy on RTE-5 data sets, they …
Building World Event Representations From Linguistic Representations
T Bruland – 2013 – brage.bibsys.no
Page 1. Building World Event Representations From Linguistic Representations Thesis for the degree of Philosophiae Doctor Trondheim, November 2013 Norwegian University of Science and Technology Faculty of Information …
Artificial conversations for chatter bots using knowledge representation, learning, and pragmatics
C Chakrabarti – 2014 – search.proquest.com
… It has several components like database access, speech synthesizer, speech recognizer, and a language understanding engine. It has achieved good results in travel reservation domain, and is available as an API to build an end to end system [68] …
Intrinsic and Extrinsic Approaches to Recognizing Textual Entailment
DP der Philosophischen – 2011 – pdfs.semanticscholar.org
Page 1. Intrinsic and Extrinsic Approaches to Recognizing Textual Entailment Dissertation zur Erlangung des akademischen Grades eines Doktors der Philosophie der Philosophischen Fakultäten der Universität des Saarlandes vorgelegt von Rui Wang March, 2011 Page 2 …
Electrical Engineering and Computer Science Department
E Tomai – 2009 – qrg.northwestern.edu
Page 1. Electrical Engineering and Computer Science Department Technical Report NWU-EECS-09-17 July 30, 2009 A Pragmatic Approach to Computational Narrative Understanding Emmett Tomai Abstract Narrative understanding …
Modelling cultural dimensions and social relationships to create cultural synthetic characters
AMM Ellafi – 2015 – ros.hw.ac.uk
Page 1. Modelling Cultural Dimensions and Social Relationships to Create Cultural Synthetic Characters Ali Mosbah Mohammed Ellafi Submitted in fulfilment of the requirements of the Degree of Doctor of Philosophy at Heriot-Watt University …
Recognizing Textual Entailment Using Description Logic And Semantic Relatedness
R Siblini – 2014 – spectrum.library.concordia.ca
Page 1. RECOGNIZING TEXTUAL ENTAILMENT USING DESCRIPTION LOGIC AND SEMANTIC RELATEDNESS Reda Siblini A thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements …