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检索条件"主题词=Natural Language Computing"

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Machine Learning Algorithms for natural language Semantics and Cognitive computing

Machine Learning Algorithms for Natural Language Semantics a...

引用

International Conference on Computational Science and Computational Intelligence (CSIC)

作者： Khan, Emdad MUM Dept Comp Sci Fairfield IA 52557 USA InternetSpeech Inc San Jose CA USA

ISBN: (纸本)9781509055104

We present elegant machine learning algorithms to efficiently learn natural language semantics (MLANLP);thus enabling much better natural language computing (NLC) and Cognitive computing (CC). Our algorithms use human brainlike learning approach and achieve very good generalization on natural language (mainly text) data. Existing machine learning algorithms performs well on numerical data and cannot easily learn semantics of natural language. Such algorithms, however, can address well some specific problems of natural language, like Name Entity Recognition where data can be easily represented by numbers and semantics between words (name and entity) are simple. Besides, the generalization capabilities of existing machine learning algorithms are limited, especially for complex data. The generalization capability for learning semantics of natural language should be very good to ensure reliable NLC and CC. Our MLANLP has good generalization capability, and can also derive new semantics and knowledge, very much needed for NLC and CC.

关键词： Machine Learning natural language computing Cognitive computing Semantic Engine Artificial Intelligence

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Dialogue Structure Annotation Guidelines for Army Research Laboratory (ARL) Human-Robot Dialogue Corpus

Dialogue Structure Annotation Guidelines for Army Research L...

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作者： Bonial,Claire,Traum,David,Henry,Traum,Lukin,Stephanie M.,Marge,Matthew,Artstein,Ron,Pollard,Kimberly A.,Foots,Ashley,Baker,Anthony L.,Voss,Clare R., CCDC Army Research Laboratory Adelphi United States

Here we provide detailed guidelines on how to annotate a multifloor human-robot dialogue for structure elements relevant to informing dialogue management in robotic systems. We start with transcribed and time-aligned dialogue data collected from participants and Wizards of Oz across multiple years of an Army Research Laboratory human-robot interaction experiment the Bot language project. We define structure elements and annotation protocol for marking up these dialogue data, with the aim to inform development of a dialogue management system onboard a robot.

关键词： Dialogue systems natural language computing Speech Robots Robotics Human machine systems Human-robot interaction Annotation Dialogue structure natural-language processing

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KaZam: An integrated inference engine for assembly

KaZam: An integrated inference engine for assembly

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作者： Wang,Qinsi, Carnegie Mellon University Pittsburgh United States

Cancer signaling is an example of a complicated system where interactions have important causal effects. Creating mechanistic models, rather than correlative models, helps with understanding such systems. The goal of the Big Mechanism is to push forward tool development to support 1 the automatic generation of these models from natural language and 2 the analysis of these models to improve understanding. The Big Mechanism project has four phases - reading, assembly, modeling, and analysis, and our efforts have been put into the later three aspects in the following ways.

关键词： Probability distributions natural language computing Differential equations Algorithms Protein-protein interactions Theoretical computer science Artificial intelligence Inference engines Computational biology Cancer

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Data Extraction from natural language using Universal Networking language 3

Data Extraction from Natural Language using Universal Networ...

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International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC)

作者： Saha, Aloke Kumar Mridha, M. F. Rafiq, Jahir Ibna Das, Jugal Krishna Univ Asia Pacific Dept Comp Sci & Enginerring Dhaka Bangladesh Jahangirnagar Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781538632437

Data extraction, which falls under the area of natural language Processing (UNL), finds specific data from unstructured data. This research paves the way to introduce a unique technique on data extraction - providing the user with exactly what is asked without any mimicry of unsolicited data. The proposal sets logical and symmetrical relation between the search criteria and operational data. Since the data is unstructured and volume can be relatively high, we have emphasized highly on putting the data under categories - defined and used by the researchers for further exploitation of data. Universal Networking language (UNL) is efficiently used to compare data and merge. A new approach of machine learning is presented herein that essentially augments efficiency of natural language computing (NLC) and Cognitive computing (CC). This proposed approach uses UNL relationship and successful test data shows much improved results and efficient generalization. Existing machine learning approaches are widely used on numeric data which are producing expected results but one key contention is the limitation of data type that can be handled. Current models fail to properly train on the semantics, logical consistency;many natural language properties are either ignored or prove too much of a task. Consequently, the approach presented herein this paper carries further positive points in producing meaningful and worthwhile result. Moreover, complex data that are consisted of alphanumeric data, sequence and resulting criteria can be executed correctly.

关键词： Big Data Data Extraction NLP Universal Networking natural language computing Machine Learning

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Data Extraction from natural language using Universal Networking language

Data Extraction from Natural Language using Universal Networ...

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International Conference on Current Trends in Computer, Electrical, Electronics and Communication

作者： Aloke Kumar Saha M. F. Mridha Jahir Ibna Rafiq Jugal Krishna Das Dept. Of Computer Science and Enginerring University of Asia Pacific Dhaka Banglaesh Dept. Of Computer Science and Enginerring Jahangirnagar University Dhaka

ISBN: (纸本)9781538632444

关键词： Big Data Data Extraction NLP Universal Networking natural language computing Machine Learning

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