'Unstructured text' presentation slideshows

A year on the Semantic Web @ W3C (with more details on RDFa)

1.19k views • 108 slides

Analyzing User-Generated Content using Econometrics

Analyzing User-Generated Content using Econometrics. Panos Ipeirotis Stern School of Business New York University. Comparative Shopping. Comparative Shopping. Are Customers Irrational?. BuyDig.com gets Price Premium (customers pay more than the minimum price). $11.04 (+1.5%).

716 views • 62 slides

Preserving the Inputs and Outputs of Scholarship

Preserving the Inputs and Outputs of Scholarship. Tim Babbitt SVP, ProQuest Platforms. Our Vision. ProQuest will be central to research around the world. The changing Context. A Revolution in Research.

478 views • 41 slides

Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers

Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers. Panos Ipeirotis Stern School of Business New York University . Joint work with Victor Sheng, Foster Provost, and Jing Wang. Motivation. Many task rely on high-quality labels for objects:

504 views • 37 slides

Extracting names and resolving identities in unstructured text

Extracting names and resolving identities in unstructured text. Carol Jean Godby Research Scientist OCLC Research. Three problems in automated name extraction. Recognize Distinguish names from non-names. Assign the name to a broadly recognized category. Cluster

380 views • 28 slides

SMART

256 views • 25 slides

Wiki3C : Exploiting Wikipedia for Context-aware Concept Categorization

Wiki3C : Exploiting Wikipedia for Context-aware Concept Categorization. Presenter : Bei -YI Jiang Authors : Peng Jiang, Huiman Hou , Lijiang Chen, Shimin Chen, Conglei Yao, Chengkai Li, Min Wang 2013. ACM. Outlines. Motivation Objectives Methodology Experiments

379 views • 20 slides

Leveraging Knowledge to Increase Responsiveness

302 views • 20 slides

Leveraging Knowledge to Increase Responsiveness

324 views • 20 slides

Data-Parallel Finite-State Machines

Data-Parallel Finite-State Machines. Todd Mytkowicz, Madanlal Musuvathi, and Wolfram Schulte Microsoft Research. New method to break data dependencies. Preserves program semantics Does not use speculation Generalizes to other domains , but this talk focuses on FSM.

399 views • 19 slides

Querying the Web for Genealogical Information

Querying the Web for Genealogical Information. Troy Walker Spring Research Conference 2003. Research funded by NSF. Genealogical Information on the Web. Hundreds of thousands of sites Some professional (Ancestry.com, Familysearch.org) Mostly hobbyist (Cyndislist.com) Search engines

389 views • 16 slides

Warranty/Performance Text Exploration for Modern Reliability

138 views • 13 slides

SMART

SMART. System Model Acquisition from Requirements Text. System Model Acquisition from Requirements Text. Operates on free text documentation, such as business process specifications or user requirements Results depend critically on the quality of the processed documentation

136 views • 13 slides

Text Mining Application Programming Chapter 1 Introduction

Text Mining Application Programming Chapter 1 Introduction. Manu Konchady, 2006. Definition: Text Mining. all types of text processing that deal with finding, organizing, and analyzing information. (formal) the creation of new information that is not obvious in a collection of documents.

124 views • 12 slides

Handling uncertainty in information extraction

209 views • 8 slides

Text Mining: Main Steps

Text Mining: Main Steps. Some analysis can be done without a document-term matrix. Obtain unstructured text. Analyze & DataViz. Convert to Structured Data. External Social Media Twitter, Web, Blogs, Patents, Reviews. Internal Records, Emails, Surveys Notebooks. Document

268 views • 5 slides

AQUAINT Questioning Answering (AQUA) System SAIC / Stanford Univ. (Knowledge System Laboratory)

AQUAINT Questioning Answering (AQUA) System SAIC / Stanford Univ. (Knowledge System Laboratory). OBJECTIVES Search and retrieve relevant text from Unstructured text Structured databases Metadata markups Informal text (I.e. chat rooms) Provide source reliability assessment

105 views • 1 slides