Applications of Word Vectors in Text Retrieval and Classificationshakimov
Applications of word vectors (word2vec, BERT, etc.) on problems such as text retrieval, classification of textual documents for tasks such as sentiment analysis, spam detection.
Conversation with-search-engines (Ren et al. 2020)Vaclav Kosar
To make voice search more natural, this paper compiles new dataset and implements two novel modules into QA architecture.
Authors: Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, Maarten de Rijke
+ Other mentioned papers
With the rise of Web 2.0, API-based software has appeared. This article examines the API-based search tool created for the Korean search engine Naver: Webonaver (Webometrics Tool for Naver). The software is able to collect large amounts of data automatically and can easily distinguish between different types of information on the web, which was impossible before. In particular, Internet researchers can improve efficiency of data analysis within a specified timeframe using this tool. This paper illustrates how to use WeboNaver and tries to verify the usability and reliability through several case studies. In this article, Korean National Assembly Members’ web presence was analyzed, as was the web presence of the term H1N1.
Web 2.0의 도래와 함께 Open API를 응용한 소프트웨어 프로그램이 등장하면서 더 이상 사용자들은 웹에서 정보를 수동으로 검색하면서 일일이 살펴보는 번거로움을 겪지 않아도 된다. 공개된 API를 활용해 몇 번의 간단한 조작으로 방대한 데이터를 체계적으로 수집하고 관리할 수 있다. 본 논문은 Open API를 응용해 개발한 검색전문 프로그램 WeboNaver(Webometrics Tool for Naver)를 소개한다. 이는 한국에서 가장 영향력 있는 검색엔진 중의 하나인 네이버를 이용해 방대한 데이터를 카테고리별로 자동수집하여 저장해주는 프로그램이다. 연구자들은 이를 활용해 데이터 관리와 처리, 분석 과정에 정확성과 고도의 효율성을 기할 수 있을 것이다. 논문의 목적은 WeboNaver의 사용을 원하는 학생, 일반인, 연구자의 이해를 돕고자 실제 사례들을 통하여 분석절차를 구체적으로 제시해 그 유용성을 입증하는 것이다. 이 프로그램을 사용하여 18대 국회의원 292명의 웹가시성을 조사하였다. 또한 신종플루와 관련된 단어들의 웹 가시성을 분석하였다.
Applications of Word Vectors in Text Retrieval and Classificationshakimov
Applications of word vectors (word2vec, BERT, etc.) on problems such as text retrieval, classification of textual documents for tasks such as sentiment analysis, spam detection.
Conversation with-search-engines (Ren et al. 2020)Vaclav Kosar
To make voice search more natural, this paper compiles new dataset and implements two novel modules into QA architecture.
Authors: Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, Maarten de Rijke
+ Other mentioned papers
With the rise of Web 2.0, API-based software has appeared. This article examines the API-based search tool created for the Korean search engine Naver: Webonaver (Webometrics Tool for Naver). The software is able to collect large amounts of data automatically and can easily distinguish between different types of information on the web, which was impossible before. In particular, Internet researchers can improve efficiency of data analysis within a specified timeframe using this tool. This paper illustrates how to use WeboNaver and tries to verify the usability and reliability through several case studies. In this article, Korean National Assembly Members’ web presence was analyzed, as was the web presence of the term H1N1.
Web 2.0의 도래와 함께 Open API를 응용한 소프트웨어 프로그램이 등장하면서 더 이상 사용자들은 웹에서 정보를 수동으로 검색하면서 일일이 살펴보는 번거로움을 겪지 않아도 된다. 공개된 API를 활용해 몇 번의 간단한 조작으로 방대한 데이터를 체계적으로 수집하고 관리할 수 있다. 본 논문은 Open API를 응용해 개발한 검색전문 프로그램 WeboNaver(Webometrics Tool for Naver)를 소개한다. 이는 한국에서 가장 영향력 있는 검색엔진 중의 하나인 네이버를 이용해 방대한 데이터를 카테고리별로 자동수집하여 저장해주는 프로그램이다. 연구자들은 이를 활용해 데이터 관리와 처리, 분석 과정에 정확성과 고도의 효율성을 기할 수 있을 것이다. 논문의 목적은 WeboNaver의 사용을 원하는 학생, 일반인, 연구자의 이해를 돕고자 실제 사례들을 통하여 분석절차를 구체적으로 제시해 그 유용성을 입증하는 것이다. 이 프로그램을 사용하여 18대 국회의원 292명의 웹가시성을 조사하였다. 또한 신종플루와 관련된 단어들의 웹 가시성을 분석하였다.
Graph Techniques for Natural Language ProcessingSujit Pal
Natural Language embodies the human ability to make “infinite use of finite means” (Humboldt, 1836; Chomsky, 1965). A relatively small number of words can be combined using a grammar in myriad different ways to convey all kinds of information. Languages model inter-relationships between their words, just like graphs model inter-relationships between their vertices. It is not surprising then, that graphs are a natural tool to study Natural Language and glean useful information from it, automatically, and at scale. This presentation will focus on NLP techniques to convert raw text to graphs, and present Graph Theory based solutions to some common NLP problems. Solutions presented will use Apache Spark or Neo4j depending on problem size and scale. Examples of Graph Theory solutions presented include PageRank for Document Summarization, Link Prediction from raw text for Knowledge Graph enhancement, Label Propagation for entity classification, and Random Walk techniques to find similar documents.
Cross-language information retrieval (CLIR) is a technique to locate documents written in one natural language by queries expressed in another language. This project investigates the feasibility of CLIR based on domain-specific bilingual corpus databases.
Representing financial reports on the semantic web a faithful translation f...Jie Bao
Jie Bao, Graham Rong, Xian Li, and Li Ding (2010). Representing Financial Reports on the Semantic Web - A Faithful Translation from XBRL to OWL. In The 4th International Web Rule Symposium (RuleML).
Fast data mining flow prototyping using IPython NotebookJimmy Lai
Big data analysis requires fast prototyping on data mining process to gain insight into data. In this slides, the author introduces how to use IPython Notebook to sketch code pieces for data mining stages and make fast observations easily.
Pane, Web e Salame 4 “Come Internet mi ha salvato la vita” - Alessandro Minin...Pane, Web & Salame
Alessandro Mininno racconta come è nato Pane, Web e Salame, come sono andate le scorse edizioni e i tre mesi di tour per cercare gli speaker di #PWES4.
http://2013.panewebesalame.com/
Graph Techniques for Natural Language ProcessingSujit Pal
Natural Language embodies the human ability to make “infinite use of finite means” (Humboldt, 1836; Chomsky, 1965). A relatively small number of words can be combined using a grammar in myriad different ways to convey all kinds of information. Languages model inter-relationships between their words, just like graphs model inter-relationships between their vertices. It is not surprising then, that graphs are a natural tool to study Natural Language and glean useful information from it, automatically, and at scale. This presentation will focus on NLP techniques to convert raw text to graphs, and present Graph Theory based solutions to some common NLP problems. Solutions presented will use Apache Spark or Neo4j depending on problem size and scale. Examples of Graph Theory solutions presented include PageRank for Document Summarization, Link Prediction from raw text for Knowledge Graph enhancement, Label Propagation for entity classification, and Random Walk techniques to find similar documents.
Cross-language information retrieval (CLIR) is a technique to locate documents written in one natural language by queries expressed in another language. This project investigates the feasibility of CLIR based on domain-specific bilingual corpus databases.
Representing financial reports on the semantic web a faithful translation f...Jie Bao
Jie Bao, Graham Rong, Xian Li, and Li Ding (2010). Representing Financial Reports on the Semantic Web - A Faithful Translation from XBRL to OWL. In The 4th International Web Rule Symposium (RuleML).
Fast data mining flow prototyping using IPython NotebookJimmy Lai
Big data analysis requires fast prototyping on data mining process to gain insight into data. In this slides, the author introduces how to use IPython Notebook to sketch code pieces for data mining stages and make fast observations easily.
Pane, Web e Salame 4 “Come Internet mi ha salvato la vita” - Alessandro Minin...Pane, Web & Salame
Alessandro Mininno racconta come è nato Pane, Web e Salame, come sono andate le scorse edizioni e i tre mesi di tour per cercare gli speaker di #PWES4.
http://2013.panewebesalame.com/
This is Part II of the tutorial "Entity Linking and Retrieval" given at WWW 2013 (together with E. Meij and D. Odijk). For the complete tutorial material (including slides for the other parts) visit http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
Reflected Intelligence: Lucene/Solr as a self-learning data systemTrey Grainger
What if your search engine could automatically tune its own domain-specific relevancy model? What if it could learn the important phrases and topics within your domain, automatically identify alternate spellings (synonyms, acronyms, and related phrases) and disambiguate multiple meanings of those phrases, learn the conceptual relationships embedded within your documents, and even use machine-learned ranking to discover the relative importance of different features and then automatically optimize its own ranking algorithms for your domain?
In this presentation, you’ll learn you how to do just that - to evolving Lucene/Solr implementations into self-learning data systems which are able to accept user queries, deliver relevance-ranked results, and automatically learn from your users’ subsequent interactions to continually deliver a more relevant experience for each keyword, category, and group of users.
Such a self-learning system leverages reflected intelligence to consistently improve its understanding of the content (documents and queries), the context of specific users, and the relevance signals present in the collective feedback from every prior user interaction with the system. Come learn how to move beyond manual relevancy tuning and toward a closed-loop system leveraging both the embedded meaning within your content and the wisdom of the crowds to automatically generate search relevancy algorithms optimized for your domain.
Multimodal Searching and Semantic Spaces: ...or how to find images of Dalmati...Jonathon Hare
Tutorial at the "Reality of the Semantic Gap in Image Retrieval" tutorial at the first international conference on Semantics And digital Media Technology (SAMT 2006). 6th December 2006.
Basic introduction to recommender systems + Implementing a content-based recommender system by leveraging knowledge encoded into Linked Open Data datasets
The increasing amount of valuable semi-structured data has become available online. In this talk, we overview the state of the art in entity ranking over structured data ("linked data").
The following presentation has been gathered through my ML internship in Shenasa-AI.
At that stage, I had to search through work done in the field of Content-Based Image Retrieval (CBIR) and present my findings to my supervisor.
The diversity and complexity of contents available on the web have dramatically increased in recent years. Multimedia content such as images, videos, maps, voice recordings has been published more often than before. Document genres have also been diversified, for instance, news, blogs, FAQs, wiki. These diversified information sources are often dealt with in a separated way. For example, in web search, users have to switch between search verticals to access different sources. Recently, there has been a growing interest in finding effective ways to aggregate these information sources so that to hide the complexity of the information spaces to users searching for relevant information. For example, so-called aggregated search investigated by the major search engine companies will provide search results from several sources in a single result page. Aggregation itself is not a new paradigm; for instance, aggregate operators are common in database technology.
This talk presents the challenges faced by the like of web search engines and digital libraries in providing the means to aggregate information from several and complex information spaces in a way that helps users in their information seeking tasks. It also discusses how other disciplines including databases, artificial intelligence, and cognitive science can be brought into building effective and efficient aggregated search systems.
Hoaxy is a tool to visualize the spread of URLs consisting low-credible web-documents. We use features related to propagation . dynamics to classify the duplicates of low-credible claims.
This is Part II of the tutorial "Entity Linking and Retrieval" given at SIGIR 2013 (together with E. Meij and D. Odijk). For the complete tutorial material (including slides for the other parts) visit http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
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.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
1. Summary of Papers of SIGIR 2011 Workshop on Query Representation and Understanding Chetana Gavankar
2. Ricardo Campos, Alipio Jorge, Gael Dias: "Using Web Snippets and Query-logs to Measure Implicit Temporal Intents in Queries"
3. Types of Temporal queries 1. Atemporal : Queries not sensitive to time like plan my trip 2. Temporal unambiguous : Queries in concrete time period. Ex : Haiti earthquake in 2010 3. Temporal ambiguous : queries with multiple instances over time. Ex : Cricket worldcup which occurs every four years.
4. Web snippets and Query Logs Content-Related Resources , based on a web content approach Simply requires the set of web search results. Query-Log Resources , based on similar year-qualified queries Imply that some versions of the query have already been issued.
5. 1. Web snippets ( temporal evidence within web pages): TA(q)= ∑ f ε I w f f(q) I = {Tsnippet(.),TTitle(.),TUrl(.)} Value each feature differently using w f 18.14 for TTitles, 50.91 for TSnippets and 30.95 for Turl(.) If TA(q) value < 10% then Atemporal. Dates appearing in query & docs may not match. # Snippets Retrieved with Dates Identifying implicit temporal queries TSnippets = # Snippets Retrieved
6. Identifying implicit temporal queries 2.Web Query Logs : Temporal activity can be recorded from date & time of request and from user activity. No. of times query is pre, post qualified by year is WA(q,y)=#(y,q) + #(q,y) α(q) = ∑ y WA (q,y) / ∑ x #(x,q) + ∑ x #(q,x) If query qualified with single year then α(q) =1
7. Results An additional analysis led us to conclude that the temporal information is more frequent in web snippets than in any of the query logs of Google and Yahoo!; Overall, while most of the queries have a TSnippet(.) value around 20%, TLogYahoo(.) and TLogGoogle(.) are mostly near to 0%.
8.
9. Query having dates does not necessarily mean that it has temporal intent (from web query logs of Google and yahoo) Ex: October Sky movie
17. Generic and domain independent 2.Peripheral lexicon (P-Lex or HEADs): Rare ones with degree much less than those in kernal P K-Lex (popular segments) P-Lex (rarer segments) how to matthew brodrick wiki accessories free police officer and who is in australia epson tx800 videos star trek next gen
18. Degree Disribution |N| = Nodes, |E| = edges C= average clustering coefficient d=mean shortest path between edges C rand and d rand are corr. Values in random graph C rand ~ k'/ |N| , d rand ~ ln(|N|)/ ln(|k'|) k' = average degree of graph Degree distribution= p(k) = nodes with degree k/ total nodes
34. Latent Topic Analysis in Query Log Query log record (user_id, query, clicked_url, time) Pseudo-document generation: Queries related to the same host are aggregated. General sites like “en.wikipedia.org” are not suitable for latent topic analysis & are eliminated Latent Dirichlet Allocation Algorithm) LDA to conduct the latent semantic topic analysis on the collection of host-based pseudo-documents. Z = set of latent topic s z i Each z i is associated with multinomial distribution of terms P ( tk | z i )= prob of term tk given topic z i
35. Personalization π u ={ π u 1 , π u 2 , … , π u |z| } = profile of the user u , π u i = P ( z i | u ) = probability that the user u prefers the topic z i Generate user-based pseudo-document U for user u . { P ( z 1 | U ), P ( z 2 | U ), … , P ( z | Z | | U )} = profile of u . candidate query q : t 1 , … t n Topic of term t r = z r
36. Topic based scoring with personalization Candidate query score: model parameter P ( zj | zi ) captures the relationship of two topics With personal profile P ( z 1 | u ) = probability that user u prefers the topic z 1
37. Conclusion Framework that considers personalization achieves the best performance. With user profiles, the topic-based scoring part is more reliable