The document summarizes an analysis of natural language processing tools to help populate an artificial intelligence simulation called Athena. It evaluated three tools from the Cognitive Computation Group: a co-reference resolution tool to identify common entities across text, a Wikifier to link terms to Wikipedia, and a named entity recognizer to label predefined entity types. While the interactive demos were useful, full access to the tools is needed to determine their applicability for Athena. Gaining access to more tools, including some on a DARPA site, is listed as a next step.
The document summarizes an entity extraction and typing framework proposed by the author. The framework constructs a heterogeneous graph connecting entity mentions, surface names, and relation phrases extracted from documents. It then performs joint type propagation and relation phrase clustering on the graph to infer types for entity mentions. Evaluation on news, tweets and reviews shows the framework outperforms existing methods in recognizing new types and domains without extensive feature engineering or human supervision. It obtains improvements by modeling each mention individually and addressing data sparsity through relation phrase clustering.
This document provides an overview of machine learning applications in natural language processing and text classification. It discusses common machine learning tasks like part-of-speech tagging, named entity extraction, and text classification. Popular machine learning algorithms for classification are described, including k-nearest neighbors, Rocchio classification, support vector machines, bagging, and boosting. The document argues that machine learning can be used to solve complex real-world problems and that text processing is one area with many potential applications of these techniques.
Advantages of nlp for business performance pptangela weeks
http://www.businessnlpacademy.co.uk/
Professionals in neurolinguistic programming recognize that 'learned limitations' hamper people from excelling in life. By teaching folks these methods, individuals learn how to become free from such limitations, and start to live more creative, authentic lives. In this manner, previous personal weaknesses can transform into powerful strengths.
Intorduction to Neuro Linguistic Programming (NLP)eohart
The document discusses Neuro-Linguistic Programming (NLP) and how it can be applied in the workplace. NLP focuses on how our neurology, language, and programming influence our behaviors and communication. The key principles of NLP discussed in the document are that people have the resources to change, behavior is geared towards adaptation, and accepting people while changing behaviors.
Slides from my lecture for the Information Retrieval and Data Mining course at University College London
The slides cover introductory concepts on topic models, vector semantics and basic end applications
The document discusses social media analytics and natural language processing. It provides an overview of social media analytics, including that it involves collecting and analyzing audience data from social networks to improve business decisions. It also discusses the seven layers of data in social media analytics, including text, networks, actions, mobile, hyperlinks, location, and search engines. It then covers topics in natural language processing like text analytics, tokenization, bag of words, TF-IDF, n-grams, stop words, stemming, and lemmatization.
Semantic Web & Information Brokering: Opportunities, Commercialization and Ch...Amit Sheth
Amit Sheth, "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges," Keynote talk at the workshop on Semantic Web: Models, Architecture and Management, September 21, 2000, Lisbon, Portugal.
This was the keynote given at probably the first international event with "Semantic Web" in title (and before the well known SciAm article). As in TBL's use of Semantic Web in his 1999 book, (semantic) metadata plays central role. The use of Worldmodel/Ontology is consistent with our use of ontology for (Web) information integration in 1994 CIKM paper. Summary of the talk by event organizers and other details are at: http://knoesis.org/library/resource.php?id=735
Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (product was called MediaAnywhere A/V search engine- discussed in this paper in the context of one of its use by a customer Redband Broadcasting). The product included Semantic Web/populated Ontology based semantic (faceted) search, semantic browsing, semantic personalization, semantic targeting (advertisement), etc as is described in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000). MediaAnywhere has about 25 ontologies in News/Business, Sports, Entertainment, etc.
Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
The document summarizes an entity extraction and typing framework proposed by the author. The framework constructs a heterogeneous graph connecting entity mentions, surface names, and relation phrases extracted from documents. It then performs joint type propagation and relation phrase clustering on the graph to infer types for entity mentions. Evaluation on news, tweets and reviews shows the framework outperforms existing methods in recognizing new types and domains without extensive feature engineering or human supervision. It obtains improvements by modeling each mention individually and addressing data sparsity through relation phrase clustering.
This document provides an overview of machine learning applications in natural language processing and text classification. It discusses common machine learning tasks like part-of-speech tagging, named entity extraction, and text classification. Popular machine learning algorithms for classification are described, including k-nearest neighbors, Rocchio classification, support vector machines, bagging, and boosting. The document argues that machine learning can be used to solve complex real-world problems and that text processing is one area with many potential applications of these techniques.
Advantages of nlp for business performance pptangela weeks
http://www.businessnlpacademy.co.uk/
Professionals in neurolinguistic programming recognize that 'learned limitations' hamper people from excelling in life. By teaching folks these methods, individuals learn how to become free from such limitations, and start to live more creative, authentic lives. In this manner, previous personal weaknesses can transform into powerful strengths.
Intorduction to Neuro Linguistic Programming (NLP)eohart
The document discusses Neuro-Linguistic Programming (NLP) and how it can be applied in the workplace. NLP focuses on how our neurology, language, and programming influence our behaviors and communication. The key principles of NLP discussed in the document are that people have the resources to change, behavior is geared towards adaptation, and accepting people while changing behaviors.
Slides from my lecture for the Information Retrieval and Data Mining course at University College London
The slides cover introductory concepts on topic models, vector semantics and basic end applications
The document discusses social media analytics and natural language processing. It provides an overview of social media analytics, including that it involves collecting and analyzing audience data from social networks to improve business decisions. It also discusses the seven layers of data in social media analytics, including text, networks, actions, mobile, hyperlinks, location, and search engines. It then covers topics in natural language processing like text analytics, tokenization, bag of words, TF-IDF, n-grams, stop words, stemming, and lemmatization.
Semantic Web & Information Brokering: Opportunities, Commercialization and Ch...Amit Sheth
Amit Sheth, "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges," Keynote talk at the workshop on Semantic Web: Models, Architecture and Management, September 21, 2000, Lisbon, Portugal.
This was the keynote given at probably the first international event with "Semantic Web" in title (and before the well known SciAm article). As in TBL's use of Semantic Web in his 1999 book, (semantic) metadata plays central role. The use of Worldmodel/Ontology is consistent with our use of ontology for (Web) information integration in 1994 CIKM paper. Summary of the talk by event organizers and other details are at: http://knoesis.org/library/resource.php?id=735
Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (product was called MediaAnywhere A/V search engine- discussed in this paper in the context of one of its use by a customer Redband Broadcasting). The product included Semantic Web/populated Ontology based semantic (faceted) search, semantic browsing, semantic personalization, semantic targeting (advertisement), etc as is described in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000). MediaAnywhere has about 25 ontologies in News/Business, Sports, Entertainment, etc.
Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
Search, Signals & Sense: An Analytics Fueled VisionSeth Grimes
The document discusses how text analytics can fuel semantic search and sensemaking by extracting features from documents, analyzing relationships between entities, and integrating search with other data sources. It outlines trends toward more unified search platforms that incorporate user context and infer intent to provide categorized, clustered results rather than just hit lists. The goal is for search to be the starting point for iterative sensemaking through analysis and synthesis of information.
Automatic indexing is the process of analyzing documents to extract information to be included in an index. This can be done through statistical, natural language, concept-based, or hypertext linkage techniques. Statistical techniques are the most common, identifying words and phrases to index documents. Natural language techniques perform additional parsing of text. Concept indexing correlates words to concepts, while hypertext linkages create connections between documents. The goal of automatic indexing is to preprocess documents to allow for relevant search results by representing concepts in the index.
The document discusses collaboration in disease surveillance and response. It describes InSTEDD's hybrid approach to disease surveillance which combines various data sources to identify health risks. It also discusses tools developed by InSTEDD like GeoChat and Mesh4x that enable real-time information sharing and collaboration between organizations responding to disease outbreaks. The document emphasizes that collaboration is critical for effective outbreak containment and humanitarian response.
InSTEDD: Collaboration in Disease Surveillance & ResponseInSTEDD
The document discusses collaboration in disease surveillance and response. It describes InSTEDD's hybrid approach to disease surveillance which combines various data sources to identify health risks. It also discusses tools developed by InSTEDD like GeoChat and Mesh4x that enable real-time information sharing and collaboration between organizations responding to disease outbreaks. The document emphasizes that collaboration is critical for effective outbreak containment and humanitarian response.
Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Several Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec Machine Learning (ML) approach are used to vectorize the project descriptions and generate semantic vectors. Each of these representations is then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE) using 300 feature dimensions and RF regression model. However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated with the project abstract.
La résolution de problèmes à l'aide de graphesData2B
This document discusses how network science can be used to analyze and draw insights from different types of data. It describes how network science is the study of networks representing physical, biological, and social phenomena. It provides examples of how network science can be applied to geographic, temporal, social, and semantic network data. The document also discusses how network science combined with data science and machine learning techniques can enable machines to perform more human-like reasoning about ambiguous or uncertain concepts.
We have envisioned that computers will understand natural language and predict what we need help in order to complete tasks via conversational interactions. This talk focuses on context-aware understanding in different levels: 1) word-level contexts in sentences 2) sentence-level contexts in dialogues. Word-level contexts contribute both semantic and syntactic relations, which benefit sense representation learning and knowledge-guided language understanding. Also, sentence-level contexts may significantly affect dialogue-level performance. This talk investigates how misunderstanding of a single-turn utterance degrades the success rate of an end-to-end reinforcement learning based dialogue system. Then we will highlight challenges and recent trends driven by deep learning and intelligent assistants.
A Model Of Opinion Mining For Classifying MoviesAndrew Molina
This document summarizes a research paper that proposes a model for classifying movies based on user opinions mined from online reviews. The model is capable of suggesting words a reviewer may use based on the title of their review. It can also intelligently predict the popularity of a movie on a scale of "super-flop" to "super-hit" by analyzing sentiments in reviews. The model was tested on over 1000 movie reviews and showed better performance at classifying less popular movies compared to popular review websites. The researchers believe this model could simplify the reviewing process by making it quicker and more effective.
The document discusses the development of corpora at the University of Nottingham, including both mono-modal corpora containing one type of data (text-based) and multi-modal corpora containing multiple data types (text, video, audio). It describes the Nottingham Multi-Modal Corpus and Nottingham Learner Corpora as examples. The Nottingham eLanguage Corpus aims to collect diverse digital language data types from individuals, including SMS, email, social media, web browsing history, and location data. One challenge is modeling how language varies based on dynamic contextual factors. As a case study, the document outlines the Thrill corpus containing synchronized audio, video and sensor data from fairground rides, to examine linguistic patterns across different phases
This document discusses opinion mining and sentiment analysis. It defines opinion mining as extracting opinions about attributes of items from text, such as reviews. Sentiment analysis involves computational analysis of subjective text to determine sentiment and track predictive judgments. Both terms have been used since the early 2000s in papers in NLP communities. Initially, sentiment analysis focused more on classifying reviews as positive or negative, but now both terms are used more broadly to mean computational analysis of opinions, sentiments, and subjectivity in text.
The document discusses how computation can accelerate the generation of new knowledge by enabling large-scale collaborative research and extracting insights from vast amounts of data. It provides examples from astronomy, physics simulations, and biomedical research where computation has allowed more data and researchers to be incorporated, advancing various fields more quickly over time. Computation allows for data sharing, analysis, and hypothesis generation at scales not previously possible.
Descartes aims to establish certainty in knowledge in response to skepticism. He subjects his own beliefs to methodological doubt, questioning all knowledge derived from the senses due to their fallibility. His goal is to find an indubitable foundation for knowledge that can withstand even the strongest skeptical challenges. While Descartes employs skepticism strategically in his method of doubt, his overall project seeks to overcome rather than affirm skepticism by discovering knowledge that is absolutely certain.
New Research Articles 2020 May Issue International Journal of Software Engin...ijseajournal
This document proposes an agent-based approach to systematically specify auditability requirements during goal-oriented requirements engineering. It presents a case study applying this approach to the design of a system called LawDisTrA that distributes lawsuits among judges in a transparent manner. The approach uses an interdependency graph to capture different facets of transparency and their operationalization. An evaluation of a implemented LawDisTrA system that distributed over 300,000 lawsuits demonstrated the ability of the presented approach to address the cross-organizational nature of transparency through adequate auditability techniques.
Acknowledgement Entity Recognition In CORD-19 PapersMartha Brown
The document describes a system called ACKEXTRACT that was developed to recognize acknowledgement entities in scholarly papers. It uses natural language processing and named entity recognition to extract persons and organizations from acknowledgement sections and footnotes. The system was evaluated on a manually labeled dataset from CORD-19 papers and achieved a high performance of F1=0.92. When applied to CORD-19 papers, it found that only 50-60% of named entities were actually acknowledged, while others were only mentioned. The extracted acknowledgement entities and code for ACKEXTRACT are publicly available online.
This document discusses various topics related to data science innovations including natural language generation, systems of insight, and deep learning. It provides an overview of these areas and references additional resources. It also discusses data science algorithms and how companies are using them to reimagine business processes. Finally, it considers the roles of statistics, data mining, and data science and how they differ in terms of the type of data and analysis used.
COMPREHENSIVE ANALYSIS OF NATURAL LANGUAGE PROCESSING TECHNIQUEJournal For Research
Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
The slides present Project Chronos' educational project in Astronomy and Aerospace Engineering, primarily from the standpoint of its informatic infrastructure. They were shown at a MeetUp in Turin, Italy, on Dec 3 2014.
In particular, the technologies are illustrated used for Text and Data Mining and the semantic description of the information stored in Project Chronos' database. The last slides (in Italian) ask the audience for ideas on how to implement Machine Learning techniques in our infrastructure.
Project Chronos' database is going to be used to create applications for educational purposes aimed at promoting public engagement in space research and strengthening ties between scientific communities and civil society.
Search, Signals & Sense: An Analytics Fueled VisionSeth Grimes
The document discusses how text analytics can fuel semantic search and sensemaking by extracting features from documents, analyzing relationships between entities, and integrating search with other data sources. It outlines trends toward more unified search platforms that incorporate user context and infer intent to provide categorized, clustered results rather than just hit lists. The goal is for search to be the starting point for iterative sensemaking through analysis and synthesis of information.
Automatic indexing is the process of analyzing documents to extract information to be included in an index. This can be done through statistical, natural language, concept-based, or hypertext linkage techniques. Statistical techniques are the most common, identifying words and phrases to index documents. Natural language techniques perform additional parsing of text. Concept indexing correlates words to concepts, while hypertext linkages create connections between documents. The goal of automatic indexing is to preprocess documents to allow for relevant search results by representing concepts in the index.
The document discusses collaboration in disease surveillance and response. It describes InSTEDD's hybrid approach to disease surveillance which combines various data sources to identify health risks. It also discusses tools developed by InSTEDD like GeoChat and Mesh4x that enable real-time information sharing and collaboration between organizations responding to disease outbreaks. The document emphasizes that collaboration is critical for effective outbreak containment and humanitarian response.
InSTEDD: Collaboration in Disease Surveillance & ResponseInSTEDD
The document discusses collaboration in disease surveillance and response. It describes InSTEDD's hybrid approach to disease surveillance which combines various data sources to identify health risks. It also discusses tools developed by InSTEDD like GeoChat and Mesh4x that enable real-time information sharing and collaboration between organizations responding to disease outbreaks. The document emphasizes that collaboration is critical for effective outbreak containment and humanitarian response.
Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Several Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec Machine Learning (ML) approach are used to vectorize the project descriptions and generate semantic vectors. Each of these representations is then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE) using 300 feature dimensions and RF regression model. However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated with the project abstract.
La résolution de problèmes à l'aide de graphesData2B
This document discusses how network science can be used to analyze and draw insights from different types of data. It describes how network science is the study of networks representing physical, biological, and social phenomena. It provides examples of how network science can be applied to geographic, temporal, social, and semantic network data. The document also discusses how network science combined with data science and machine learning techniques can enable machines to perform more human-like reasoning about ambiguous or uncertain concepts.
We have envisioned that computers will understand natural language and predict what we need help in order to complete tasks via conversational interactions. This talk focuses on context-aware understanding in different levels: 1) word-level contexts in sentences 2) sentence-level contexts in dialogues. Word-level contexts contribute both semantic and syntactic relations, which benefit sense representation learning and knowledge-guided language understanding. Also, sentence-level contexts may significantly affect dialogue-level performance. This talk investigates how misunderstanding of a single-turn utterance degrades the success rate of an end-to-end reinforcement learning based dialogue system. Then we will highlight challenges and recent trends driven by deep learning and intelligent assistants.
A Model Of Opinion Mining For Classifying MoviesAndrew Molina
This document summarizes a research paper that proposes a model for classifying movies based on user opinions mined from online reviews. The model is capable of suggesting words a reviewer may use based on the title of their review. It can also intelligently predict the popularity of a movie on a scale of "super-flop" to "super-hit" by analyzing sentiments in reviews. The model was tested on over 1000 movie reviews and showed better performance at classifying less popular movies compared to popular review websites. The researchers believe this model could simplify the reviewing process by making it quicker and more effective.
The document discusses the development of corpora at the University of Nottingham, including both mono-modal corpora containing one type of data (text-based) and multi-modal corpora containing multiple data types (text, video, audio). It describes the Nottingham Multi-Modal Corpus and Nottingham Learner Corpora as examples. The Nottingham eLanguage Corpus aims to collect diverse digital language data types from individuals, including SMS, email, social media, web browsing history, and location data. One challenge is modeling how language varies based on dynamic contextual factors. As a case study, the document outlines the Thrill corpus containing synchronized audio, video and sensor data from fairground rides, to examine linguistic patterns across different phases
This document discusses opinion mining and sentiment analysis. It defines opinion mining as extracting opinions about attributes of items from text, such as reviews. Sentiment analysis involves computational analysis of subjective text to determine sentiment and track predictive judgments. Both terms have been used since the early 2000s in papers in NLP communities. Initially, sentiment analysis focused more on classifying reviews as positive or negative, but now both terms are used more broadly to mean computational analysis of opinions, sentiments, and subjectivity in text.
The document discusses how computation can accelerate the generation of new knowledge by enabling large-scale collaborative research and extracting insights from vast amounts of data. It provides examples from astronomy, physics simulations, and biomedical research where computation has allowed more data and researchers to be incorporated, advancing various fields more quickly over time. Computation allows for data sharing, analysis, and hypothesis generation at scales not previously possible.
Descartes aims to establish certainty in knowledge in response to skepticism. He subjects his own beliefs to methodological doubt, questioning all knowledge derived from the senses due to their fallibility. His goal is to find an indubitable foundation for knowledge that can withstand even the strongest skeptical challenges. While Descartes employs skepticism strategically in his method of doubt, his overall project seeks to overcome rather than affirm skepticism by discovering knowledge that is absolutely certain.
New Research Articles 2020 May Issue International Journal of Software Engin...ijseajournal
This document proposes an agent-based approach to systematically specify auditability requirements during goal-oriented requirements engineering. It presents a case study applying this approach to the design of a system called LawDisTrA that distributes lawsuits among judges in a transparent manner. The approach uses an interdependency graph to capture different facets of transparency and their operationalization. An evaluation of a implemented LawDisTrA system that distributed over 300,000 lawsuits demonstrated the ability of the presented approach to address the cross-organizational nature of transparency through adequate auditability techniques.
Acknowledgement Entity Recognition In CORD-19 PapersMartha Brown
The document describes a system called ACKEXTRACT that was developed to recognize acknowledgement entities in scholarly papers. It uses natural language processing and named entity recognition to extract persons and organizations from acknowledgement sections and footnotes. The system was evaluated on a manually labeled dataset from CORD-19 papers and achieved a high performance of F1=0.92. When applied to CORD-19 papers, it found that only 50-60% of named entities were actually acknowledged, while others were only mentioned. The extracted acknowledgement entities and code for ACKEXTRACT are publicly available online.
This document discusses various topics related to data science innovations including natural language generation, systems of insight, and deep learning. It provides an overview of these areas and references additional resources. It also discusses data science algorithms and how companies are using them to reimagine business processes. Finally, it considers the roles of statistics, data mining, and data science and how they differ in terms of the type of data and analysis used.
COMPREHENSIVE ANALYSIS OF NATURAL LANGUAGE PROCESSING TECHNIQUEJournal For Research
Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
The slides present Project Chronos' educational project in Astronomy and Aerospace Engineering, primarily from the standpoint of its informatic infrastructure. They were shown at a MeetUp in Turin, Italy, on Dec 3 2014.
In particular, the technologies are illustrated used for Text and Data Mining and the semantic description of the information stored in Project Chronos' database. The last slides (in Italian) ask the audience for ideas on how to implement Machine Learning techniques in our infrastructure.
Project Chronos' database is going to be used to create applications for educational purposes aimed at promoting public engagement in space research and strengthening ties between scientific communities and civil society.
2. Outline:
Purpose of the Analysis
Athena—Many Hours of Research
Natural-language processing (NLP)
Analysis: Constraints, Limitations, and Assumptions
Co-reference Resolution Tool
Wikifier
Named Entity Recognizer
Next Steps
3. Purpose of the Analysis:
The purpose of this analysis was to nominate NLP tools that will
help researchers and analysts at TRADOC G2 M&SD locate data
and information culled from documents to populate their human,
social, behavior, and culture simulation called Athena.
In coordination with researchers from the TRADOC G2 Modeling
and Simulation Directorate (M&SD) housed at Fort Leavenworth,
students from the spring 2015 semester course titled “Software
Development and Design” at the University of Saint Mary in
Leavenworth, Kansas analyzed several natural-language
processing (NLP) tools from the Cognitive Computation Group
(CCG) at the University of Illinois at Urbana-Champaign.
4. Athena—Many Hours of Research
Athena is a software application that enables analysts and
commanders to simulate the Political, Military, Economic,
Social, Infrastructure, and Information (PMESII) entities and
processes within the context of a battlefield environment, a
wide-area security operation, or in support of a country study
to evaluate social evolution dynamics.
Needs to be populated with entities such as actor, civilian
group, force group, message, belief system, and others.
Athena researchers troll sometimes a hundred documents or
more looking for relevant entities and relationships… MANY,
MANY HOURS…
6. Natural-language Processing (NLP):
SHORT VERSION: NLP is the ability of a computer to understand a
language just like a human can understand a language.
LONG VERSION: NLP is a field of computer science, artificial
intelligence, and computational linguistics concerned with the
interactions between computers and human (natural) languages.
One important goal of NLP is trying to get computational systems to
“understand” the meaning (semantics) and context of words,
sentences, and other linguistic devices in much the same way that a
human mind is able to do so.
Cognitive Computation Group (CCG) at the University of Illinois at
Urbana-Champaign
Co-Reference Resolution | Name Entity Recognizer | Wikifier
7.
8. Analysis: Constraints, Limitations, and
Assumptions
Time, because the project
began near the end of the
spring semester, giving
the team only six weeks
to do the work
The fact that the team
could only work together
two days a week, even
though the students from
USM worked many more
hours every week
The fact that the lead of
the team took on a new
job half way through the
work
The fact that the team
could only access
online interactive
demonstration versions
of CCG’s tools, rather
than full access to the
complete tools
Although some of the
tools were discovered
listed on DARPA’s DEFT
site, there was no way
to access them, and the
site administrator never
responded to the team’s
email requesting
access
Most members of the team
have limited experience
with NLP tools
Access to NLP tools will be
limited
It is not likely that any of
the tools inspected this go
around will serve the
purpose of helping
researchers and analysts at
the TRADOC G2 M&SD
locate data and information
to populate Athena
9. Co-reference Resolution Tool:
Description and Purpose
A given entity—representing a person, a location, or an organization, for
example—may be mentioned in a text in multiple, ambiguous ways. The
Co-reference Resolution Tool processes unannotated text, detecting
mentions of entities and showing which mentions are co-referential (i.e., all
words, phrases, or expressions that refer to the same entity in a text). The
purpose of this tool is to help parse documents for common entities and
represent the document in a diagram form. The interactive demo consists
of a box into which text is placed and a button that says Submit. Once text
is entered and the Submit button is pressed, within a few seconds the
demo displays the parsed results.
10. “Helicopters will patrol the temporary no-fly zone around New Jersey's MetLife Stadium Sunday, with F-16s based in Atlantic
City ready to be scrambled if an unauthorized aircraft does enter the restricted airspace. Down below, bomb-sniffing dogs will
patrol the trains and buses that are expected to take approximately 30,000 of the 80,000-plus spectators to Sunday's Super
Bowl between the Denver Broncos and Seattle Seahawks. The Transportation Security Administration said it has added about
two dozen dogs to monitor passengers coming in and out of the airport around the Super Bowl. On Saturday, TSA agents
demonstrated how the dogs can sniff out many different types of explosives. Once they do, they're trained to sit rather than
attack, so as not to raise suspicion or create a panic. TSA spokeswoman Lisa Farbstein said the dogs undergo 12 weeks of
training, which costs about $200,000, factoring in food, vehicles and salaries for trainers. Dogs have been used in cargo areas
for some time, but have just been introduced recently in passenger areas at Newark and JFK airports. JFK has one dog and
Newark has a handful, Farbstein said.”
11. “Helicopters will patrol
the temporary no-fly
zone around New
Jersey's MetLife
Stadium Sunday…”
Bad
Really
Good
Good
Bad
Not Sure, so Bad
13. Wikifier: Parses a text and links terms to Wikipedia
“Helicopters will patrol the temporary no-fly zone around New Jersey's MetLife Stadium Sunday,
with F-16s based in Atlantic City ready to be scrambled if an unauthorized aircraft does enter the
restricted airspace. Down below, bomb-sniffing dogs will patrol the trains and buses that are
expected to take approximately 30,000 of the 80,000-plus spectators to Sunday's Super Bowl
between the Denver Broncos and Seattle Seahawks.”
15. Named Entity Recognizer:
The Named Entity
Recognizer tool labels
eighteen predefined types
of entities in plain text, all
shown in the image to the
left. The purpose of this
tool is simply to tell you
whether any terms in the
parsed text falls under one
of the eighteen different
entity types. Simply add
text in the box provided
and press Submit.