Deception detection, or Deceptive opinion detection, is the task of inferring and deciding whether a given text, that carries some opinion is deceptive (or “false”). To further clear what this means, take, for instance, an hotel review site - an “adversary” may post a review that was deliberately written to sound authentic and to deceive the reader that this review is indeed truthful. The `deception` we will be referring to in this summary will be of user reviews / opinions..
Comparative Study on Lexicon-based sentiment analysers over Negative sentimentAI Publications
Sentiment Analysis or Opinion Mining is one of the latest trends of social listening, which is presently reshaping Commercial Organisations. It is a significant task of Natural Language Processing (NLP). The vast availability of product review data within Social media like Twitter, Facebook, and e-commerce site like Amazon, Alibaba. An organisation can get insight into a customer's mind based on a product or what type of opinion the product has generated in the market. Accordingly, an organisation can take some reactive preventive measures. While analysing the above, we have found that negative opinion has a strong effect on customers' minds than the positive one. Also, negative opinions are more viral in terms of diffusion. Our present work is based on a comparison of two available rule-based Sentiment analysers, VADER, and TextBlob on domain-specific product review data from Amazon.co.in. It investigates, which has higher accuracy in terms of classifying negative opinions. Our research has found out that VADER’s negative polarity sentiment classification accuracy is more elevated than TextBlob.
(from HBS Working Knowledge - http://hbswk.hbs.edu/item/6430.html) Why do platforms that deliberately restrict consumer choice do just as well, if not better, than platforms offering unlimited choice? Examples abound in dating services, executive recruitment, and real-estate brokerage. In the online dating market, for instance, eHarmony restricts potential candidates for its members while rival Match.com lets its users browse as many profiles as they like. In the working paper "Platforms and Limits to Network Effects," HBS professors Hanna Halaburda and Mikolaj Jan Piskorski discuss competitive strategy in these environments and the options available to platform designers and proprietors who want to maximize the strength of network effects.
Increasing interpreting needs a more objective and automatic measurement. We hold a basic idea that 'translating means translating meaning' in that we can assessment interpretation quality by comparing the
meaning of the interpreting output with the source input. That is, a translation unit of a 'chunk' named
Frame which comes from frame semantics and its components named Frame Elements (FEs) which comes
from Frame Net are proposed to explore their matching rate between target and source texts. A case study in this paper verifies the usability of semi-automatic graded semantic-scoring measurement for human
simultaneous interpreting and shows how to use frame and FE matches to score. Experiments results show that the semantic-scoring metrics have a significantly correlation coefficient with human judgment.
A method to identify potential ambiguous malay words through ambiguity attrib...csandit
We describe here a methodology to identify a list of ambiguous Malay words that are commonly
being used in Malay documentations such as Requirement Specification. We compiled several
relevant and appropriate requirement quality attributes and sentence rules from previous
literatures and adopt it to come out with a set of ambiguity attributes that most suit Malay
words. The extracted Malay ambiguous words (potential) are then being mapped onto the
constructed ambiguity attributes to confirm their vagueness. The list is then verified by Malay
linguist experts. This paper aims to identify a list of potential ambiguous words in Malay as an
attempt to assist writers to avoid using the vague words while documenting Malay Requirement
Specification as well as to any other related Malay documentation. The result of this study is a
list of 120 potential ambiguous Malay words that could act as guidelines in writing Malay
sentences.
GENERATING SUMMARIES USING SENTENCE COMPRESSION AND STATISTICAL MEASURESijnlc
In this paper, we propose a compression based multi-document summarization technique by incorporating
word bigram probability and word co-occurrence measure. First we implemented a graph based technique
to achieve sentence compression and information fusion. In the second step, we use hand-crafted rule
based syntactic constraint to prune our compressed sentences. Finally we use probabilistic measure while
exploiting word co-occurrence within a sentence to obtain our summaries. The system can generate summaries for any user-defined compression rate.
This paper introduces a novel approach to tackle the existing gap on message translations in dialogue systems. Currently, submitted messages to the dialogue systems are considered as isolated sentences. Thus, missing context information impede the disambiguation of homographs words in ambiguous sentences. Our approach solves this disambiguation problem by using concepts over existing ontologies.
Comparative Study on Lexicon-based sentiment analysers over Negative sentimentAI Publications
Sentiment Analysis or Opinion Mining is one of the latest trends of social listening, which is presently reshaping Commercial Organisations. It is a significant task of Natural Language Processing (NLP). The vast availability of product review data within Social media like Twitter, Facebook, and e-commerce site like Amazon, Alibaba. An organisation can get insight into a customer's mind based on a product or what type of opinion the product has generated in the market. Accordingly, an organisation can take some reactive preventive measures. While analysing the above, we have found that negative opinion has a strong effect on customers' minds than the positive one. Also, negative opinions are more viral in terms of diffusion. Our present work is based on a comparison of two available rule-based Sentiment analysers, VADER, and TextBlob on domain-specific product review data from Amazon.co.in. It investigates, which has higher accuracy in terms of classifying negative opinions. Our research has found out that VADER’s negative polarity sentiment classification accuracy is more elevated than TextBlob.
(from HBS Working Knowledge - http://hbswk.hbs.edu/item/6430.html) Why do platforms that deliberately restrict consumer choice do just as well, if not better, than platforms offering unlimited choice? Examples abound in dating services, executive recruitment, and real-estate brokerage. In the online dating market, for instance, eHarmony restricts potential candidates for its members while rival Match.com lets its users browse as many profiles as they like. In the working paper "Platforms and Limits to Network Effects," HBS professors Hanna Halaburda and Mikolaj Jan Piskorski discuss competitive strategy in these environments and the options available to platform designers and proprietors who want to maximize the strength of network effects.
Increasing interpreting needs a more objective and automatic measurement. We hold a basic idea that 'translating means translating meaning' in that we can assessment interpretation quality by comparing the
meaning of the interpreting output with the source input. That is, a translation unit of a 'chunk' named
Frame which comes from frame semantics and its components named Frame Elements (FEs) which comes
from Frame Net are proposed to explore their matching rate between target and source texts. A case study in this paper verifies the usability of semi-automatic graded semantic-scoring measurement for human
simultaneous interpreting and shows how to use frame and FE matches to score. Experiments results show that the semantic-scoring metrics have a significantly correlation coefficient with human judgment.
A method to identify potential ambiguous malay words through ambiguity attrib...csandit
We describe here a methodology to identify a list of ambiguous Malay words that are commonly
being used in Malay documentations such as Requirement Specification. We compiled several
relevant and appropriate requirement quality attributes and sentence rules from previous
literatures and adopt it to come out with a set of ambiguity attributes that most suit Malay
words. The extracted Malay ambiguous words (potential) are then being mapped onto the
constructed ambiguity attributes to confirm their vagueness. The list is then verified by Malay
linguist experts. This paper aims to identify a list of potential ambiguous words in Malay as an
attempt to assist writers to avoid using the vague words while documenting Malay Requirement
Specification as well as to any other related Malay documentation. The result of this study is a
list of 120 potential ambiguous Malay words that could act as guidelines in writing Malay
sentences.
GENERATING SUMMARIES USING SENTENCE COMPRESSION AND STATISTICAL MEASURESijnlc
In this paper, we propose a compression based multi-document summarization technique by incorporating
word bigram probability and word co-occurrence measure. First we implemented a graph based technique
to achieve sentence compression and information fusion. In the second step, we use hand-crafted rule
based syntactic constraint to prune our compressed sentences. Finally we use probabilistic measure while
exploiting word co-occurrence within a sentence to obtain our summaries. The system can generate summaries for any user-defined compression rate.
This paper introduces a novel approach to tackle the existing gap on message translations in dialogue systems. Currently, submitted messages to the dialogue systems are considered as isolated sentences. Thus, missing context information impede the disambiguation of homographs words in ambiguous sentences. Our approach solves this disambiguation problem by using concepts over existing ontologies.
A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet IJECEIAES
Arabic Sentiment analysis research field has been progressing in a slow pace compared to English and other languages. In addition to that most of the contributions are based on using supervised machine learning algorithms while comparing the performance of different classifiers with different selected stylistic and syntactic features. In this paper, we presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features. Moreover, we provided a lexicon dataset of around 69 k unique concepts that covers multi-domain reviews collected from the internet. We also tested the lexicon on a test sample from the dataset it was collected from and obtained an accuracy of 70%. The lexicon has been made publicly available for scientific purposes.
Convincing a customer is always considered as a challenging task in every business. But when it comes to
online business, this task becomes even more difficult. Online retailers try everything possible to gain the
trust of the customer. One of the solutions is to provide an area for existing users to leave their comments.
This service can effectively develop the trust of the customer however normally the customer comments
about the product in their native language using Roman script. If there are hundreds of comments this
makes difficulty even for the native customers to make a buying decision. This research proposes a system
which extracts the comments posted in Roman Urdu, translate them, find their polarity and then gives us
the rating of the product. This rating will help the native and non-native customers to make buying decision
efficiently from the comments posted in Roman Urdu.
We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed
similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Using NLP Approach for Analyzing Customer Reviews cscpconf
The Web considers one of the main sources of customer opinions and reviews which they are
represented in two formats; structured data (numeric ratings) and unstructured data (textual
comments). Millions of textual comments about goods and services are posted on the web by
customers and every day thousands are added, make it a big challenge to read and understand
them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those
opinions and reviews. In this paper, we use natural language processing techniques to generate
some rules to help us understand customer opinions and reviews (textual comments) written in
the Arabic language for the purpose of understanding each one of them and then convert them
to a structured data. We use adjectives as a key point to highlight important information in the
text then we work around them to tag attributes that describe the subject of the reviews, and we
associate them with their values (adjectives).
Aspect Level Sentiment Analysis for Arabic LanguageMido Razaz
This is the presentation I used in my proposal seminar for master degree in ISSR.
the thesis about Aspect Level Sentiment Classification for Arabic Language.
Any further info. please contact me at (razaz_2006@hotmail.com)
Customer Opinions Evaluation: A Case Study on Arabic Tweets gerogepatton
This paper presents an automatic method for extracting, processing, and analysis of customer opinions
on Arabic social media. We present a four-step approach for mining of Arabic tweets. First, Natural
Language Processing (NLP) with different types of analyses had performed. Second, we present an
automatic and expandable lexicon for Arabic adjectives. The initial lexicon is built using 1350 adjectives
as seeds from processing of different datasets in Arabic language. The lexicon is automatically expanded
by collecting synonyms and morphemes of each word through Arabic resources and google translate.
Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method. Finally, Feature Selection (FS) is also considered to enhance the mining results. The
experimental results reveal that the proposed method outperforms counterpart ones with an improvement
margin of up to 4% using F-Measure.
CUSTOMER OPINIONS EVALUATION: A CASESTUDY ON ARABIC TWEETSgerogepatton
This paper presents an automatic method for extracting, processing, and analysis of customer opinions on Arabic social media. We present a four-step approach for mining of Arabic tweets. First, Natural Language Processing (NLP) with different types of analyses had performed. Second, we present an automatic and expandable lexicon for Arabic adjectives. The initial lexicon is built using 1350 adjectives as seeds from processing of different datasets in Arabic language. The lexicon is automatically expanded by collecting synonyms and morphemes of each word through Arabic resources and google translate. Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method. Finally, Feature Selection (FS) is also considered to enhance the mining results. The experimental results reveal that the proposed method outperforms counterpart ones with an improvement margin of up to 4% using F-Measure.
CUSTOMER OPINIONS EVALUATION: A CASESTUDY ON ARABIC TWEETSijaia
This paper presents an automatic method for extracting, processing, and analysis of customer opinions on Arabic social media. We present a four-step approach for mining of Arabic tweets. First, Natural Language Processing (NLP) with different types of analyses had performed. Second, we present an automatic and expandable lexicon for Arabic adjectives. The initial lexicon is built using 1350 adjectives as seeds from processing of different datasets in Arabic language. The lexicon is automatically expanded by collecting synonyms and morphemes of each word through Arabic resources and google translate. Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method. Finally, Feature Selection (FS) is also considered to enhance the mining results. The experimental results reveal that the proposed method outperforms counterpart ones with an improvement margin of up to 4% using F-Measure.
Twitter, has fast emerged as one of the most powerful social media sites which can
sway opinions. Sentiment or opinion analysis has of late emerged one of the most
researched and talked about subject in Natural Language Processing (NLP), thanks
mainly to sites like Twitter. In the past, sentiment analysis models using Twitter data have
been built to predict sales performance, rank products and merchants, public opinion
polls, predict election results, political standpoints, predict box-office revenues for movies
and even predict the stock market. This study proposes a general frame in R programming
language to act as a gateway for the analysis of the tweets that portray emotions in a
short and concentrated format. The target tweets include brief emotion descriptions and
words that are not used with a proper format or grammatical structure. Majority of the
work constituted in Turkish includes the data scope and the aim of preparing a data-set.
There is no concrete and usable work done on Turkish Tweet sentiment analysis as a
software client/web application. This study is a starting point on building up the next
steps. The aim is to compare five different common machine learning methods (support
vector machines, random forests, boosting, maximum entropy, and artificial neural
networks) to classify Twitters sentiments
Marxism in the internet age and social networksYoav Francis
[Paper is in Hebrew]
An analysis of Marx's theory applicability for the internet age (including startups and freelancers), and analysis of social networks and how, instead of creating, they prevent class consciousness.
A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet IJECEIAES
Arabic Sentiment analysis research field has been progressing in a slow pace compared to English and other languages. In addition to that most of the contributions are based on using supervised machine learning algorithms while comparing the performance of different classifiers with different selected stylistic and syntactic features. In this paper, we presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features. Moreover, we provided a lexicon dataset of around 69 k unique concepts that covers multi-domain reviews collected from the internet. We also tested the lexicon on a test sample from the dataset it was collected from and obtained an accuracy of 70%. The lexicon has been made publicly available for scientific purposes.
Convincing a customer is always considered as a challenging task in every business. But when it comes to
online business, this task becomes even more difficult. Online retailers try everything possible to gain the
trust of the customer. One of the solutions is to provide an area for existing users to leave their comments.
This service can effectively develop the trust of the customer however normally the customer comments
about the product in their native language using Roman script. If there are hundreds of comments this
makes difficulty even for the native customers to make a buying decision. This research proposes a system
which extracts the comments posted in Roman Urdu, translate them, find their polarity and then gives us
the rating of the product. This rating will help the native and non-native customers to make buying decision
efficiently from the comments posted in Roman Urdu.
We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed
similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Using NLP Approach for Analyzing Customer Reviews cscpconf
The Web considers one of the main sources of customer opinions and reviews which they are
represented in two formats; structured data (numeric ratings) and unstructured data (textual
comments). Millions of textual comments about goods and services are posted on the web by
customers and every day thousands are added, make it a big challenge to read and understand
them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those
opinions and reviews. In this paper, we use natural language processing techniques to generate
some rules to help us understand customer opinions and reviews (textual comments) written in
the Arabic language for the purpose of understanding each one of them and then convert them
to a structured data. We use adjectives as a key point to highlight important information in the
text then we work around them to tag attributes that describe the subject of the reviews, and we
associate them with their values (adjectives).
Aspect Level Sentiment Analysis for Arabic LanguageMido Razaz
This is the presentation I used in my proposal seminar for master degree in ISSR.
the thesis about Aspect Level Sentiment Classification for Arabic Language.
Any further info. please contact me at (razaz_2006@hotmail.com)
Customer Opinions Evaluation: A Case Study on Arabic Tweets gerogepatton
This paper presents an automatic method for extracting, processing, and analysis of customer opinions
on Arabic social media. We present a four-step approach for mining of Arabic tweets. First, Natural
Language Processing (NLP) with different types of analyses had performed. Second, we present an
automatic and expandable lexicon for Arabic adjectives. The initial lexicon is built using 1350 adjectives
as seeds from processing of different datasets in Arabic language. The lexicon is automatically expanded
by collecting synonyms and morphemes of each word through Arabic resources and google translate.
Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method. Finally, Feature Selection (FS) is also considered to enhance the mining results. The
experimental results reveal that the proposed method outperforms counterpart ones with an improvement
margin of up to 4% using F-Measure.
CUSTOMER OPINIONS EVALUATION: A CASESTUDY ON ARABIC TWEETSgerogepatton
This paper presents an automatic method for extracting, processing, and analysis of customer opinions on Arabic social media. We present a four-step approach for mining of Arabic tweets. First, Natural Language Processing (NLP) with different types of analyses had performed. Second, we present an automatic and expandable lexicon for Arabic adjectives. The initial lexicon is built using 1350 adjectives as seeds from processing of different datasets in Arabic language. The lexicon is automatically expanded by collecting synonyms and morphemes of each word through Arabic resources and google translate. Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method. Finally, Feature Selection (FS) is also considered to enhance the mining results. The experimental results reveal that the proposed method outperforms counterpart ones with an improvement margin of up to 4% using F-Measure.
CUSTOMER OPINIONS EVALUATION: A CASESTUDY ON ARABIC TWEETSijaia
This paper presents an automatic method for extracting, processing, and analysis of customer opinions on Arabic social media. We present a four-step approach for mining of Arabic tweets. First, Natural Language Processing (NLP) with different types of analyses had performed. Second, we present an automatic and expandable lexicon for Arabic adjectives. The initial lexicon is built using 1350 adjectives as seeds from processing of different datasets in Arabic language. The lexicon is automatically expanded by collecting synonyms and morphemes of each word through Arabic resources and google translate. Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method. Finally, Feature Selection (FS) is also considered to enhance the mining results. The experimental results reveal that the proposed method outperforms counterpart ones with an improvement margin of up to 4% using F-Measure.
Twitter, has fast emerged as one of the most powerful social media sites which can
sway opinions. Sentiment or opinion analysis has of late emerged one of the most
researched and talked about subject in Natural Language Processing (NLP), thanks
mainly to sites like Twitter. In the past, sentiment analysis models using Twitter data have
been built to predict sales performance, rank products and merchants, public opinion
polls, predict election results, political standpoints, predict box-office revenues for movies
and even predict the stock market. This study proposes a general frame in R programming
language to act as a gateway for the analysis of the tweets that portray emotions in a
short and concentrated format. The target tweets include brief emotion descriptions and
words that are not used with a proper format or grammatical structure. Majority of the
work constituted in Turkish includes the data scope and the aim of preparing a data-set.
There is no concrete and usable work done on Turkish Tweet sentiment analysis as a
software client/web application. This study is a starting point on building up the next
steps. The aim is to compare five different common machine learning methods (support
vector machines, random forests, boosting, maximum entropy, and artificial neural
networks) to classify Twitters sentiments
Marxism in the internet age and social networksYoav Francis
[Paper is in Hebrew]
An analysis of Marx's theory applicability for the internet age (including startups and freelancers), and analysis of social networks and how, instead of creating, they prevent class consciousness.
States of Mind: can they be communicated and compared?Yoav Francis
This is a dialectical discussion in the question whether or not states of mind - be them perceptive, sensational or emotional, can be compared and communicated by an agent.
[This paper is in Hebrew]
Carnivores: Inspection under Philosophy of ActionYoav Francis
A dialectic review of the act of eating animals in the context of Philosophy of Action, takne into account views by Davidson, Holton and Stocker
[This paper is in Hebrew]
From Hierarchical to a One-Level view of Consciousness: Overview and ComparisonYoav Francis
An overview and comparison between hierarchical stands (in particular, Higher-order perception (HOP) and Higher-order thought (HOT) by Armstrong and Rosenthal) to a one-level, intrinsic view of consciousness, par Husserl and Bertrano.
[This paper is in Hebrew]
Theories of Consciousness - Overview and DiscussionYoav Francis
[Paper is in Hebrew]
Overview of hierarchical theories for consciousness, in particlular Armstrong's HOP theory and Rosenthal's HOT theory, and a discussion and analysis of the theories.
Overview of the solution for Josephus problem where every third person is eliminated, followed by a solution for the general case (arbitrary "q" where every q'th person is eliminated).
In addition a short discussion of interesting problem deriving from Josephus Problem.
Durkheim, Weber and Comte: Comparative Analysis and AnalysisYoav Francis
[This paper is in Hebrew]
A short comparative analysis of Emil Durkheim and Max Weber on individualism vs. collectivism, along with a short critical analysis on the theory of August Comte.
השוואה קצרה בין משנתם של דורקהיים וובר על סוגיית תפיסת מעמד החברה כהסבר סוציולוגי (אינדיבידואליזם אל מול קולקטיביזם), וכן קטע ביקורתי קצר על נדבכים מסוימים ממשנתו של אוגוסט קונט.
Wii Sensor Bar Positioning in 3D SpaceYoav Francis
In this project we demonstrated the ability to provide a 3 dimensional human-computer interface input/interaction mechanism using simple setup comprised of 2 fixed WiiMote and a moving light source. This can be further improved by adding additional sensors to the moving light source to give a rich input mechanism with virtual or real 3D space. While the work we did does not give a robust implementation, with relatively simple techniques it can be aggregated to create an accurate and responsive 3D input setup with relatively low cast (at about $21 per WiiMote). It is to be seen what application could be created for such a setup utilizing the technique discussed here.
Fisheye State Routing (FSR) - Protocol OverviewYoav Francis
Overview of the Fisheye State Routing (FSR) for cellular networks, IDC 2012
By Yoav Francis and Nir Solomon
(Part of a performance comparison of various routing algorithms in cellular networks)
By Nir Solomon, Yoav Francis and Liahav Eitan
Abstract:
One of greatest applicative benefits of SDN is enhancement of network security by making the network react to threats in real-time using data from all the switches in the network. For example, the OpenFlow Controller (OFC) can identify a DDoS attack on the network and divert or block traffic in an adaptive manner.
Unfortunately, OpenFlow also introduces a new threat to network security – attacks on the OFC itself, the “soft-belly” in regards to network security in SDN. The controller, by being responsible for multiple switches, is a `high-valued` target (a single point-of-failure), and we aim to understand better its vulnerability to DDoS attacks.
DDoS on the OFC can affect the entire network in several ways, depending on the OpenFlow Applications in the network and the level of dependency of the OF Switches on the OFC:
1. The entire network might be slowed down and suffer from packet-loss.
2. Some packets might be handled normally while others are mishandled by switches in the network, depending on the OpenFlow Applications that apply to these packets and whether they require communication with the OFC.
3. The entire network might stop functioning.
All of the above share a unique property that does not apply in ordinary DDoS attacks: even if only one or two switches are being flooded, the entire network can be affected.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
2. Part 1 - Topics of Interest
1. Sentiment Analysis
2. Sign Language (capture and recognition)
3. Computational Creative Naming
4. Computerized Deception Detection
5. NLP Approaches for Multiword Expressions
6. Answer Extraction
7. Natural Language Generation
8. Automatic Text Summarization
9. NLP-based Bibliometrics
10. Natural Language User Interfaces for Relational Databases
11. Truecasing - Restoring Case Information for badly/non-cased text
12. The Web as a Corpus
2
3. Part 2 - Extension on 4 Selected Topics
1 - Sign Language (capture and recognition)
The American Sign language is the primary means of communication for around 1.5 million
deaf people in the United States [2].
It is a visual-gestural language using upper body
gestures. There is no written form of sign language - currently corpora take the form of
videos [13]- and the NLP eld may need to adapt for this research eld. There were a few
attempts to create a sign language corpora ([13]) , but they have yet to be learned from a
linguistic / NLP perspective. Tools and adaptation of existing tools need to be developed
in order to face this challenge - in regards to timing, spatial reference, inection and new
methods of unied motion capture for use in sign language analysis.
2 - Truecasing
Truecasing is the problem of determining proper capitalization for a sentence/document
when it is uncapitlized / wrongly capitalized. This is mainly for use in English and any
language whose script includes a distinction between lower and upper case letters.
The
problem is irrelevant for languages that are not written in Latin, Cyrillic, Greek or Armenian alphabet. Truecasing is an aid for many tasks (besides readability, of course) such as
entity recognition, translation and content extraction. The process main aim is to restore
case information to raw text. ([11, 14])
3 -Natural Language User Interfaces for Relational Databases
A Natural language interface for a database allows the user to type in natural language
queries (such as : what buses leave on 16:00 from Tel-Aviv?) - and are the transformed
to an SQL query. This translation phase poses as an NLP challenge in some regards - it
requires a morphological and syntactic analysis, followed by a semantic analysis in order to
transform the user's question input to a few intermediate-language representations - that
correlate to the possible options for the user's question, before choosing the one that will be
transformed to an SQL query. This architecture is formally known as Natural Language
Interface for Databases (NLIDB). A popular implementation of such an NLIDB is called
Edite and is widely available. ([10, 15])
4 - Computerized Deception Detection
Computerized detection of deception is the process of detecting authenticity and truthfulness in a given text (for example, someone writing false reviews). Methods for doing
so can be simply lexical (in a sense that they simply use dictionary word count), or using
POS tagging and n-grams for higher rate of success. Some previous insights include, for
example, that deceivers use verbs and pronouns more often.
More complex approaches
to yield better detection rate include referring to the syntactic stylometry of the text, by
using CFG trees. Uses for this detection can be implemented for detecting fake reviews
(Opinion Spam) ([4, 16, 17])
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4. Part 3 - 2-Page Survey - Computerized Deception Detection
Deception detection, or Deceptive opinion detection, is the task of inferring and deciding
whether a given text, that carries some opinion is deceptive (or false). To further clear
what this means, take, for instance, an hotel review site - an adversary may post a review
that was deliberately written to sound authentic and to deceive the reader that this review
is indeed truthful. The `deception` we will be referring to in this summary will be of user
reviews / opinions..
The task at hand therefore is, given some text (or review), to decide whether the review
is truthful. The need for this is rather clear - preventing deceptive opinion spam [17] in
mediums where reviews or opinions are written or posted. Nowdays, where crowdsourcing
platforms such Amazon Mechanical Turk exists, deceiving opinions can easily be generated
and can bias a user for the better (or for worst).
The task poses as quite a challenge - since we do want to reach as few false-positives
as possible, and the task itself involves many aspects from the eld of natural language
processing.
As with many other natural-language-based tasks, this tasks also requires some data - for
example, from some review websites (in [4], for example, data from tripadvisor was taken).
We need some `reviews` that are guaranteed to be truthful and some that are guaranteed
to be deceptive - that is , that can be used as a gold data-set that we can compare our
evaluation against. It is worth noting that even without an applicable gold dataset, there
exists an heuristic approach for evaluation ([19]).
In turning to evaluate deceiving reviews - we shall regard the case that such a gold-set exists
(in [17], the gold-sets for deceiving reviews were generated by using Amazon Mechanical
Turk).
As for the 'truthful' part of the gold set, that is, truthful reviews - that can be
collected from authenticated and well-reputated users (that was also done in [17]). Such
datasets, that can be domain-specic, are publicly available ([20])
Before attempting to do a machine based evaluation, it is interesting to inspect the performance of human evaluation. In [17] it is summarized that humans judgement/detection of
deceit is poor, and according to their test a maximum average accuracy of 61% of correctly
telling truth from deceit - concluding that the correlation between same/dierent decisions
by dierent people regarding a given review is almost at-chance.
As for an automated, NLP-approch for the issue - There exist several approaches :
One approach is based on analyzing the frequency of POS tagging as a comparison basis
for deciding whether a given text is deceitful or truthful. In the analysis of this method in
[17] it was shown to have the lowest accuracy from all machine-based methods.
A second approach is based on psycholinguistics in order to be able to detect personality
traits. such tool widely exists (LIWC , [21]) . It is basically a bit more socially-oriented
approach to the previous POS tagging mechanism. Analysis of this method yielded a bit
better results than the POS-based one.
A third approach introduces n-grams to the model, and categorization of the text. Using
this type of classication dramatically increased the success of detection and yielded an
accuracy of ~88%. This signies the fact that the context of words in the sentence (that
is, n-grams based detection) is a major contributor when detecting deceiving opinions.
4
5. Finally, a lately published article [4] suggested an even more novel approach - taking into
account the `syntactic stylometry` (that is, evaluating the similarity of dierent opinions
based on the 'style of writing'.
According to [22], Similar work in regards to syntactic
stylometry has been made in regards to authorship attribution and even age attribution
for blogs [23].
This more novel method can be achieved with techniques based on Probabilistic Context
Free Grammar (PCFG) parse trees - as this is the most prominent technique for analysis
of syntactic stylometry[17, 22, 23].
Previously mentioned methods are based only on
shallow lexico-syntactic features. In [4], analysis of this method yielded very high statistical
evidence of deep syntactic patterns that allow us to detect deceitful texts with very high
accuracy (91.2%)
It is also worth noting that in all machine-models suggested above, the precision and recall
parameters were very close to each other, as can be seen in the comparison table in [17].
Further research has also been made in regards to duplicate opinion detection (in a sense
that the same writer wrote duplicate reviews, but wrote each in a `dierent way`), and
specic deception detection techniques that can be model-specic ([18])
As a quick test to the reader and to signify the (lack-of ) human evaluation skills of deceit have a look at gure 1 and see if you can tell which review is truthful and which is deceitful
(this was taken from[17]).
Figure 1: Truthful and Deceitful Reviews/Opinions
1. I have stayed at many hotels traveling for both business and pleasure and I can honestly stay that The
James is tops. The service at the hotel is rst class. The rooms are modern and very comfortable. The
location is perfect within walking distance to all of the great sights and restaurants. Highly recommend
to both business travelers and couples.
2. My husband and I stayed at the James Chicago Hotel for our anniversary. This place is fantastic! We knew as soon as we arrived we made the right choice! The rooms are BEAUTIFUL and the
sta very attentive and wonderful!! The area of the hotel is great, since I love to shop I couldn't ask for
more!! We will denatly be back to Chicago and we will for sure be back to the James Chicago.
Future work obviously includes adapting the above methods to other problem domains,
for example, reviews of other kinds, or any platform where user feedback and opinion is
possible. Deception is a rather prevalent phenomenon ([24]) - in many mediums where users
can express their opinions. Another interesting direction would be to analyze deception and
truthfulness on combined data from many dierent data sets (for example, hotel reviews,
movie reviews, products, etc.) and seeing whether we can come up with a valid deception
criteria for some text from the aforementioned domains, and not from a specic domain
based on that domain training.
Personally and to conclude - I found the deception detection topic and its regards to NLP
quite fascinating, and very much enjoyed reading the relevant papers on the subject. It
seems like we are `almost-there` on creating and streamlining a product that will be able
to detect deceiving opinions on the web (or anywhere else)
5
6. Part 4 - References
[1] Becky Sue Parton, Sign Language Recognition and Translation:
A Multidisciplined
Approach From the Field of Articial Intelligence, Journal of Deaf Studies, 2011
[2] Lu and Huenerfauth, Collecting a Motion-Capture Corpus of American Sign Language
for Data-Driven Generation Research, NAACL HLT, 2010
[3] Ozbal and Strapparava, A Computational Approach to the Automation of Creative
Naming, ACL 2012
[4] Feng, Banerjee and Choi, Syntactic Stylometry for Deception Detection, ACL 2012
[5] Sag, Baldwin et al. , Multiword Expressions: A Pain in the Neck for NLP, Stanford
University LinGO Project, 2001
[6] Abney, Collins and Singhal, Answer Extraction, ATT Shannon Labs, ANLC 2000
[7] Reiter and Dale, Building Natural Language Generation Systems, Cambridge Press,
2000
[8] Hahn and Reimer, Advances in automatic text summarization, MIT Press, 1999
[9] Abu-Jbara, Ezra and Radev, Purpose and Polarity of Citation: Towards NLP-based
Bibliometrics, NAACL-HLT 2013
[10] Filipe and Mamede, Databases and Natural Language Interfaces, CSTC Portugal,
2007
[11] Lita, Roukos et al., tRuEcasIng, ACL 2003
[12] Kilgarri and Grefenstette, Introduction to the Special Issue on the Web as Corpus,
ACL 2003
[13] Segouat and Braort, Toward Categorization of Sign Language Corpora, AFNLP 2009
[14] English Wikipedia, Truecasing
[15] Stratica, Kosseim and Desai, NLIDB Templates for Semantic Parsing, Concordia University, Canada
[16] Argamon, Koppel and Avneri, Style-based Text Categorization: What Newspaper Am
I Reading?, AAAI 1998
[17] Ott et al., Finding Deceptive Opinion Spam by Any Stretch of the Imagination, ACL
2011
[18] Jindal and Liu, Opinion Spam and Analysis, WSDM 2008
[19] Wu et.
al, Distortion as a Validation Criterion in the Identication of Suspicious
Reviews, SOMA 2010
[20] TripAdvisor Ireland Dataset, http://mlg.ucd.ie/datasets/trip
[21] Linguistic Inquiry and Word Count (LIWC) - http://www.liwc.net/
[22] Hollingsworth, Syntactic Stylometry: Using Sentence Structure for Authorship Attribution, University of Georgia, 2012
[23] Jaget Sastry, Blogger Age Attribution Using Syntactic Stylometry, https://bitbucket.org/jagatsastry/
[24] Ott, Cardie and Hancock, Estimating the prevalence of deception in online review
communities, WWW 2012
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