What is involved in Natural language processing
Find out what the related areas are that Natural language processing connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Natural language processing thinking-frame.
How far is your company on its Natural language processing journey?
Take this short survey to gauge your organization’s progress toward Natural language processing leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.
To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.
Start the Checklist
Below you will find a quick checklist designed to help you think about which Natural language processing related domains to cover and 126 essential critical questions to check off in that domain.
The following domains are covered:
Natural language processing, Text simplification, Multi-document summarization, Computer-assisted reviewing, Referring expression, Part-of-speech tagging, Corner case, Punctuation mark, Language technology, Speech segmentation, Syntax guessing, Full stop, Stop words, Machine translation, Thought experiment, Machine learning, Noam Chomsky, Bag-of-words model, Relationship extraction, Open-world assumption, Supervised learning, Unsupervised learning, Speech synthesis, Speech act, Poverty of the stimulus, Discourse analysis, Sentence extraction, Automated essay scoring, Predictive text, Spell checker, Natural-language processing, Computing Machinery and Intelligence, Concept mining, First-order logic, Closed-world assumption, Statistical models, Computer-assisted translation, European Union, Text to speech, Latent semantic indexing, Hidden Markov models, Automated online assistant, Automatic identification and data capture, Rogerian psychotherapy, Shallow parsing, Spoken dialogue system, Inflectional morphology, Semantic folding, Sentence breaking, Moore’s law, Part of speech, Semi-supervised learning, Transformational grammar, Blocks world, Speech corpus, Query expansion, Collocation extraction, Textual entailment, Example-based machine translation, Named entity recognition, Information extraction, Corpus linguistics:
Natural language processing Critical Criteria:
Chart Natural language processing visions and look at the big picture.
– Does Natural language processing include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?
– How can you measure Natural language processing in a systematic way?
– How can skill-level changes improve Natural language processing?
Text simplification Critical Criteria:
Boost Text simplification outcomes and get answers.
– What are the top 3 things at the forefront of our Natural language processing agendas for the next 3 years?
– What sources do you use to gather information for a Natural language processing study?
– What are our Natural language processing Processes?
Multi-document summarization Critical Criteria:
Weigh in on Multi-document summarization governance and adopt an insight outlook.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Natural language processing?
– What prevents me from making the changes I know will make me a more effective Natural language processing leader?
– What is Effective Natural language processing?
Computer-assisted reviewing Critical Criteria:
Dissect Computer-assisted reviewing risks and describe which business rules are needed as Computer-assisted reviewing interface.
– What are our needs in relation to Natural language processing skills, labor, equipment, and markets?
– Will Natural language processing deliverables need to be tested and, if so, by whom?
– What are the business goals Natural language processing is aiming to achieve?
Referring expression Critical Criteria:
Investigate Referring expression tactics and describe which business rules are needed as Referring expression interface.
– What are the disruptive Natural language processing technologies that enable our organization to radically change our business processes?
– Do several people in different organizational units assist with the Natural language processing process?
– How can we improve Natural language processing?
Part-of-speech tagging Critical Criteria:
Co-operate on Part-of-speech tagging leadership and give examples utilizing a core of simple Part-of-speech tagging skills.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Natural language processing. How do we gain traction?
– What other jobs or tasks affect the performance of the steps in the Natural language processing process?
Corner case Critical Criteria:
Guard Corner case planning and create a map for yourself.
– How likely is the current Natural language processing plan to come in on schedule or on budget?
– Can Management personnel recognize the monetary benefit of Natural language processing?
Punctuation mark Critical Criteria:
Learn from Punctuation mark failures and remodel and develop an effective Punctuation mark strategy.
– Can we do Natural language processing without complex (expensive) analysis?
– How do we go about Comparing Natural language processing approaches/solutions?
– Are we Assessing Natural language processing and Risk?
Language technology Critical Criteria:
Look at Language technology planning and customize techniques for implementing Language technology controls.
– Will Natural language processing have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Are there any disadvantages to implementing Natural language processing? There might be some that are less obvious?
– How will you measure your Natural language processing effectiveness?
Speech segmentation Critical Criteria:
Graph Speech segmentation strategies and summarize a clear Speech segmentation focus.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Natural language processing in a volatile global economy?
– When a Natural language processing manager recognizes a problem, what options are available?
Syntax guessing Critical Criteria:
Deliberate Syntax guessing outcomes and diversify disclosure of information – dealing with confidential Syntax guessing information.
– Have you identified your Natural language processing key performance indicators?
– How do we Lead with Natural language processing in Mind?
Full stop Critical Criteria:
Contribute to Full stop leadership and drive action.
– Does Natural language processing create potential expectations in other areas that need to be recognized and considered?
– Are accountability and ownership for Natural language processing clearly defined?
Stop words Critical Criteria:
Cut a stake in Stop words planning and find out.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Natural language processing services/products?
– Risk factors: what are the characteristics of Natural language processing that make it risky?
– Do Natural language processing rules make a reasonable demand on a users capabilities?
Machine translation Critical Criteria:
Chart Machine translation risks and secure Machine translation creativity.
– For your Natural language processing project, identify and describe the business environment. is there more than one layer to the business environment?
– Does Natural language processing appropriately measure and monitor risk?
– Why are Natural language processing skills important?
Thought experiment Critical Criteria:
Chart Thought experiment strategies and document what potential Thought experiment megatrends could make our business model obsolete.
– Do those selected for the Natural language processing team have a good general understanding of what Natural language processing is all about?
– Does Natural language processing analysis isolate the fundamental causes of problems?
– Which individuals, teams or departments will be involved in Natural language processing?
Machine learning Critical Criteria:
Focus on Machine learning management and ask questions.
– Can we add value to the current Natural language processing decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– What will drive Natural language processing change?
Noam Chomsky Critical Criteria:
Study Noam Chomsky projects and create Noam Chomsky explanations for all managers.
– How can you negotiate Natural language processing successfully with a stubborn boss, an irate client, or a deceitful coworker?
– Is Natural language processing Realistic, or are you setting yourself up for failure?
Bag-of-words model Critical Criteria:
Closely inspect Bag-of-words model planning and shift your focus.
– Think about the kind of project structure that would be appropriate for your Natural language processing project. should it be formal and complex, or can it be less formal and relatively simple?
– Is maximizing Natural language processing protection the same as minimizing Natural language processing loss?
– How to Secure Natural language processing?
Relationship extraction Critical Criteria:
Boost Relationship extraction quality and learn.
– Are there Natural language processing Models?
Open-world assumption Critical Criteria:
Give examples of Open-world assumption adoptions and separate what are the business goals Open-world assumption is aiming to achieve.
Supervised learning Critical Criteria:
Illustrate Supervised learning management and reduce Supervised learning costs.
– How do we Identify specific Natural language processing investment and emerging trends?
– Who sets the Natural language processing standards?
– What about Natural language processing Analysis of results?
Unsupervised learning Critical Criteria:
Mine Unsupervised learning projects and probe Unsupervised learning strategic alliances.
– What tools and technologies are needed for a custom Natural language processing project?
– What is our Natural language processing Strategy?
Speech synthesis Critical Criteria:
Talk about Speech synthesis tasks and ask what if.
– Why should we adopt a Natural language processing framework?
Speech act Critical Criteria:
Transcribe Speech act goals and budget for Speech act challenges.
– What role does communication play in the success or failure of a Natural language processing project?
– Is Natural language processing Required?
Poverty of the stimulus Critical Criteria:
Own Poverty of the stimulus goals and revise understanding of Poverty of the stimulus architectures.
– Who will be responsible for documenting the Natural language processing requirements in detail?
– How will you know that the Natural language processing project has been successful?
– How can the value of Natural language processing be defined?
Discourse analysis Critical Criteria:
Survey Discourse analysis projects and catalog Discourse analysis activities.
– Why is it important to have senior management support for a Natural language processing project?
– What are the record-keeping requirements of Natural language processing activities?
Sentence extraction Critical Criteria:
Powwow over Sentence extraction failures and look in other fields.
– what is the best design framework for Natural language processing organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– To what extent does management recognize Natural language processing as a tool to increase the results?
Automated essay scoring Critical Criteria:
Cut a stake in Automated essay scoring adoptions and stake your claim.
– How do we maintain Natural language processings Integrity?
Predictive text Critical Criteria:
Powwow over Predictive text adoptions and revise understanding of Predictive text architectures.
– How do your measurements capture actionable Natural language processing information for use in exceeding your customers expectations and securing your customers engagement?
– How can we incorporate support to ensure safe and effective use of Natural language processing into the services that we provide?
Spell checker Critical Criteria:
Think carefully about Spell checker leadership and oversee implementation of Spell checker.
Natural-language processing Critical Criteria:
Confer re Natural-language processing management and frame using storytelling to create more compelling Natural-language processing projects.
– Who will be responsible for making the decisions to include or exclude requested changes once Natural language processing is underway?
– What vendors make products that address the Natural language processing needs?
Computing Machinery and Intelligence Critical Criteria:
Model after Computing Machinery and Intelligence strategies and observe effective Computing Machinery and Intelligence.
– Think about the functions involved in your Natural language processing project. what processes flow from these functions?
– What are the Key enablers to make this Natural language processing move?
Concept mining Critical Criteria:
Jump start Concept mining failures and explain and analyze the challenges of Concept mining.
– Think of your Natural language processing project. what are the main functions?
First-order logic Critical Criteria:
Pay attention to First-order logic outcomes and frame using storytelling to create more compelling First-order logic projects.
Closed-world assumption Critical Criteria:
Distinguish Closed-world assumption adoptions and modify and define the unique characteristics of interactive Closed-world assumption projects.
– How do senior leaders actions reflect a commitment to the organizations Natural language processing values?
– What potential environmental factors impact the Natural language processing effort?
– How do we keep improving Natural language processing?
Statistical models Critical Criteria:
Collaborate on Statistical models issues and prioritize challenges of Statistical models.
– How do we make it meaningful in connecting Natural language processing with what users do day-to-day?
Computer-assisted translation Critical Criteria:
Check Computer-assisted translation adoptions and adjust implementation of Computer-assisted translation.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Natural language processing process. ask yourself: are the records needed as inputs to the Natural language processing process available?
– What are the key elements of your Natural language processing performance improvement system, including your evaluation, organizational learning, and innovation processes?
European Union Critical Criteria:
Huddle over European Union governance and remodel and develop an effective European Union strategy.
– What are your current levels and trends in key measures or indicators of Natural language processing product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
Text to speech Critical Criteria:
Discuss Text to speech issues and acquire concise Text to speech education.
– What are our best practices for minimizing Natural language processing project risk, while demonstrating incremental value and quick wins throughout the Natural language processing project lifecycle?
– Does Natural language processing systematically track and analyze outcomes for accountability and quality improvement?
Latent semantic indexing Critical Criteria:
Consult on Latent semantic indexing tactics and budget for Latent semantic indexing challenges.
– In what ways are Natural language processing vendors and us interacting to ensure safe and effective use?
– How does the organization define, manage, and improve its Natural language processing processes?
– How is the value delivered by Natural language processing being measured?
Hidden Markov models Critical Criteria:
Study Hidden Markov models visions and spearhead techniques for implementing Hidden Markov models.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Natural language processing processes?
– Do we all define Natural language processing in the same way?
Automated online assistant Critical Criteria:
Merge Automated online assistant governance and budget the knowledge transfer for any interested in Automated online assistant.
– Is there any existing Natural language processing governance structure?
– What are specific Natural language processing Rules to follow?
Automatic identification and data capture Critical Criteria:
Contribute to Automatic identification and data capture risks and secure Automatic identification and data capture creativity.
– Consider your own Natural language processing project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
Rogerian psychotherapy Critical Criteria:
Infer Rogerian psychotherapy quality and optimize Rogerian psychotherapy leadership as a key to advancement.
– Which Natural language processing goals are the most important?
Shallow parsing Critical Criteria:
Consider Shallow parsing tasks and get the big picture.
– How do we ensure that implementations of Natural language processing products are done in a way that ensures safety?
– How do we measure improved Natural language processing service perception, and satisfaction?
Spoken dialogue system Critical Criteria:
Test Spoken dialogue system results and report on setting up Spoken dialogue system without losing ground.
– What are the barriers to increased Natural language processing production?
– Is the scope of Natural language processing defined?
Inflectional morphology Critical Criteria:
Think about Inflectional morphology projects and find the ideas you already have.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Natural language processing?
Semantic folding Critical Criteria:
Rank Semantic folding management and inform on and uncover unspoken needs and breakthrough Semantic folding results.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Natural language processing processes?
Sentence breaking Critical Criteria:
Deliberate over Sentence breaking tactics and report on setting up Sentence breaking without losing ground.
Moore’s law Critical Criteria:
Probe Moore’s law issues and check on ways to get started with Moore’s law.
Part of speech Critical Criteria:
X-ray Part of speech failures and correct better engagement with Part of speech results.
Semi-supervised learning Critical Criteria:
Focus on Semi-supervised learning management and prioritize challenges of Semi-supervised learning.
– What business benefits will Natural language processing goals deliver if achieved?
– What are current Natural language processing Paradigms?
Transformational grammar Critical Criteria:
Collaborate on Transformational grammar decisions and handle a jump-start course to Transformational grammar.
Blocks world Critical Criteria:
Deliberate over Blocks world visions and modify and define the unique characteristics of interactive Blocks world projects.
– What is our formula for success in Natural language processing ?
Speech corpus Critical Criteria:
Model after Speech corpus planning and figure out ways to motivate other Speech corpus users.
Query expansion Critical Criteria:
Coach on Query expansion outcomes and forecast involvement of future Query expansion projects in development.
– How much does Natural language processing help?
Collocation extraction Critical Criteria:
Scan Collocation extraction risks and be persistent.
Textual entailment Critical Criteria:
Derive from Textual entailment management and explore and align the progress in Textual entailment.
– What are the usability implications of Natural language processing actions?
– Are there recognized Natural language processing problems?
Example-based machine translation Critical Criteria:
Wrangle Example-based machine translation governance and inform on and uncover unspoken needs and breakthrough Example-based machine translation results.
Named entity recognition Critical Criteria:
Exchange ideas about Named entity recognition leadership and sort Named entity recognition activities.
– Think about the people you identified for your Natural language processing project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?
Information extraction Critical Criteria:
Deduce Information extraction risks and create Information extraction explanations for all managers.
– How do you determine the key elements that affect Natural language processing workforce satisfaction? how are these elements determined for different workforce groups and segments?
Corpus linguistics Critical Criteria:
Face Corpus linguistics tactics and triple focus on important concepts of Corpus linguistics relationship management.
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Natural language processing Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | http://theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Natural language processing External links:
Natural Language Processing – Research at Google
Amazon Comprehend – Natural Language Processing …
Text simplification External links:
Automatic text simplification (eBook, 2017) [WorldCat.org]
Multi-document summarization External links:
[PDF]Topic-Focused Multi-document Summarization …
[1506.01597] Abstractive Multi-Document Summarization …
[PDF]Arabic/English Multi-document Summarization …
Referring expression External links:
Referring Expression – GM-RKB – Gabor Melli
[PDF]REFERRING EXPRESSIONS, PREDICATES, …
Part-of-speech tagging External links:
[PDF]Part-of-Speech Tagging for Bengali – DCU School of …
“Part-of-Speech Tagging Guidelines for the Penn …
[PDF]Part-of-Speech Tagging – UMD Department of …
Corner case External links:
What is corner case testing? – Quora
Corner Case Technologies – Home | Facebook
Punctuation mark External links:
Approximate average frequencies for English punctuation marks per 1000 words based on 723,000 words of assorted texts are as follows: 
http://) period/full stop 65.
Language technology External links:
LiLT (Linguistic Issues in Language Technology)
Home | Anderson Language Technology Center (ALTEC) | …
Language Technology – Cherokee Nation
Speech segmentation External links:
12-5-12 speech segmentation Flashcards | Quizlet
Speech Segmentation by Native and Non-Native Speakers
Speech segmentation | Psych 256: Cognitive Psychology …
Full stop External links:
Full stop – definition of full stop by The Free Dictionary
Urban Dictionary: full stop
Stop words External links:
List of SEO Stop Words | JacobStoops.com
Avoid Stop Words In Titles | Innovation Simple
Stop words for SEO – list of Stop Words to avoid
Machine translation External links:
Machine Translation – Microsoft Translator
MyMemory – Machine translation meets human translation
Amazon Translate – Neural Machine Translation – AWS
Thought experiment External links:
The Experience Machine Thought Experiment – Wait But …
Changing baseball: A thought experiment – Red Reporter
Thought Experiment | Definition of Thought Experiment …
Machine learning External links:
What is machine learning? – Definition from WhatIs.com
DataRobot – Automated Machine Learning for Predictive …
Machine Learning Server Overview – microsoft.com
Bag-of-words model External links:
A Gentle Introduction to the Bag-of-Words Model – …
Bag-of-words model – MATLAB
Relationship extraction External links:
Relationship Extraction from Unstructured Text Based …
“Biomedical Relationship Extraction from Literature …
Supervised learning External links:
1. Supervised learning — scikit-learn 0.19.1 documentation
Unsupervised learning External links:
Unsupervised Learning of Depth and Ego-Motion from …
Speech synthesis External links:
Lyrebird – An API for Speech Synthesis
[1703.10135] Tacotron: Towards End-to-End Speech Synthesis
Speech act External links:
Speech act theory | philosophy | Britannica.com
Poverty of the stimulus External links:
[PDF]Poverty of the stimulus effects in second language …
Discourse analysis External links:
Discourse Analysis Flashcards | Quizlet
Discourse Analysis – Upcoming Events
[PDF]Discourse Analysis and Functional Grammar in the …
Sentence extraction External links:
Summarization beyond sentence extraction: A …
Automated essay scoring External links:
The Hewlett Foundation: Automated Essay Scoring | Kaggle
Automated Essay Scoring | Measurement Incorporated
Predictive text External links:
GitHub – jbrew/pt-voicebox: predictive text interface
Textgain – Predictive Text Analytics & Profiling
Spell checker External links:
French spell checker – grammar and spell check – Reverso
Spell checker – grammar and spell check in English – Reverso
Spell Check Solutions, Spell Checker | Spellchecker.net
Computing Machinery and Intelligence External links:
computing machinery and intelligence – a.m. turing, 1950
Alan Turing (Author of Computing machinery and intelligence)
[PDF]Computing Machinery and Intelligence
First-order logic External links:
What is first-order logic? – Definition from WhatIs.com
[PDF]First-Order Logic (FOL) Constant symbols aka. …
Statistical models External links:
[PDF]Statistical Models in R
Statistical models (eBook, 2003) [WorldCat.org]
Statistical Models. (eBook, 2003) [WorldCat.org]
Computer-assisted translation External links:
Computer-Assisted Translation at Smartcat
Computer-Assisted Translation (CAT) Tool | Smartling
Best Computer-Assisted Translation Software in 2018 | …
European Union External links:
EUROPA – European Union website, the official EU website
European Union (EU) Export Certificate List
Text to speech External links:
Free Text to Speech Online with Natural Voices
See screenshots, read the latest customer reviews, and compare ratings for Convert Text to Speech. Download this app from Microsoft Store for …
Jan 08, 2018 · Google text to speech powers applications to read the text on your screen aloud. For example, it can be used by: • Google Play Books to …
Latent semantic indexing External links:
What is Latent Semantic Indexing (LSI)? | HigherVisibility
[PDF]Latent Semantic Indexing: An overview
Hidden Markov models External links:
[PPT]Hidden Markov Models – IGB
[PDF]Hidden Markov Models – Princeton University
Automated online assistant External links:
Print on Demand Service & Automated online assistant …
Automated Online Assistant by Christian Sabit on Prezi
Automatic identification and data capture External links:
Automatic Identification and Data Capture (AIDC) – AIM
Rogerian psychotherapy External links:
Rogerian psychotherapy | Behavenet
Person-centered therapy (Rogerian Psychotherapy) – …
Shallow parsing External links:
Memory-Based Shallow Parsing – Internet Archive
Spoken dialogue system External links:
[PDF]A Trialogue-Based Spoken Dialogue System for …
Inflectional morphology External links:
Definition and Examples of Inflectional Morphology
Semantic folding External links:
[PDF]Semantic Folding Theory – arXiv
Moore’s law External links:
Moore’s Law | Definition of Moore’s Law by Merriam-Webster
Moore’s Law – Investopedia
Part of speech External links:
Language Log: What part of speech is “the”?
A part of speech (Book, 1980) [WorldCat.org]
Semi-supervised learning External links:
Good Semi-supervised Learning that Requires a Bad …
Transformational grammar External links:
Transformational grammar | Britannica.com
ERIC – English Transformational Grammar., 1968
Transformational Grammar | Definition of …
Blocks world External links:
Blocks World – The Cutting Room Floor – tcrf.net
Speech corpus External links:
NOIZEUS: Noisy speech corpus – Univ. Texas-Dallas
Textual entailment External links:
TextInfer 2011 Workshop on Textual Entailment – Google …
Named entity recognition External links:
NAMED ENTITY RECOGNITION – Microsoft Corporation
[PDF]NAMED ENTITY RECOGNITION – CSE, IIT Bombay
[PDF]A survey of named entity recognition and classification
Information extraction External links:
[PDF]Title: Information Extraction from Muon …
http://Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP).
Information Extraction — NYU Scholars
Corpus linguistics External links:
American Association for Corpus Linguistics …
Corpus Linguistics – Google+
Corpus Linguistics | 2017 Linguistic Institute