Legal Language Explorer .com Tutorial
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This document provides a brief tutorial for the Legal Language Explorer interface. It explains that the interface allows searching the text of US Supreme Court decisions from 1791-2005. It details how users can view frequency plots for search terms, change the years displayed, use advanced search features, normalize results, change graph types, export data, access case lists and full texts. The interface provides tools to analyze trends in legal language and concepts over time.
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About Your Signature Assignment
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Assignment Steps
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non-terminal:
WEBPAGE -> <body> { TEXT } </body>
TEXT -> STRING | <b> TEXT </b> | <i> TEXT </i> | <ul> { LISTITEM } </ul>
LISTITEM -> <li> TEXT </li>
Note that { and } are meta-symbols in E-BNF.
An example expression in the language is as follows:
<body> google <b><i><b> yahoo</b></i></b></body>
This programming project is broken down into the following series of
tasks.
1
1. (3 points) Write a class Token that can store the value and category
of any token.
2. (7 points) Develop a lexical analyzer. You should start by drawing
on paper a finite state automaton that can recognize types of tokens
and then convert the automaton into code. Name the class Lexer and
ensure that it has a public method nextToken() which returns a To-
ken. Lexer should have a constructor with the signature Lexer(String
input), where input is the string representing the query to be parsed.
If nextToken() is called when no input is left, then it should return a
token with category EOI (EndOfInput). If the next terminal string is
not considered a token by the lexical syntax, then return a token with
category Invalid.
3. (10 points) Write a syntactic analyzer which parses the tokens given by
Lexer using the recursive descent technique. Name this class Parser.
Like Lexer, Parser should have a public constructor with the signature
Parser(String input). This input string should be used to create a
Lexer object. There should also be a public method run() that will
start the parse.
As you parse the input, Parser should output (by writing to Sys-
tem.out) the token that was matched in a new line ...
Accessibility awareness brown bag
Provides an accessibility quiz and answers, defines accessibility, describes the requirements of Section 508, includes accessible user interface design suggestions, and lists accessibility tools.
Digital + Container List
This document outlines a process for integrating a finding aid (series inventory) with digital objects in a CONTENTdm collection. It describes exporting metadata from CONTENTdm, transforming it with an XSLT stylesheet to include URLs, and inserting the results into the finding aid using Microsoft Word mail merge to link containers to digital files. The end result is an EAD finding aid with integrated links from the container list to digital objects.
Nirma
This document discusses the benefits of using XML for managing dynamic documents. It notes that current solutions are only viable for static documents, and problems arise when dealing with dynamic content that multiple authors are editing simultaneously. XML allows for parallel workflows, consistent content across authoring tools, and integrated information from various sources. The document provides examples of how XML provides benefits like reusable content, high quality images, and the ability to deliver customized content to different media.
Fun with Text - Managing Text Analytics
A lecture on text analytics - 3 types of opportunities, 3 use cases, 3 dos and 3 don'ts.
Get the hang of how to go about solving a text-related business problem using text analytics.
Xpresion Manual3
The document provides an overview of the menus and screens in the Xpresion software. It includes summaries of master data entry screens for zones, countries, locations, products, and more. It also summarizes screens for customer, vendor, and contract data, as well as delivery route configuration. The document aims to familiarize the user with the available data entry options and reports in Xpresion.
Week 2 Project - STAT 3001Student Name Type your name here.docx
Week 2 Project - STAT 3001
Student Name: <Type your name here>
Date: <Enter the date on which you began working on this assignment.>
Instructions: To complete this project, you will need the following materials:
· STATDISK User Manual (found in the classroom in DocSharing)
· Access to the Internet to download the STATDISK program.
This assignment is worth a total of 60 points.
Part I. Histograms and Frequency Tables
Instructions
Answers
1. Open the file Diamonds using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about diamonds. What are the names of the variables in this file?
2. Create a histogram for the depth of the diamonds using the Auto-fit option. Paste the chart here. Once your histogram displays, click Turn on Labels to get the height of the bars.
3. Using the information in the above histogram, complete this table. Be sure to include frequency, relative frequency, and cumulative frequency.
Depth
Frequency
Relative Frequency
Cumulative Frequency
57-58.9
59-60.9
61-62.9
63-64.9
a. Using the frequency table above, how many of the diamonds have a depth of 60.9 or less? How do you know?
b. Using the frequency table above, how many of the diamonds have a depth between 59 and 62.9? Show your work.
c. What percent of the diamonds have a depth of 61 or more?
Part II. Comparing Datasets
Instructions
Answers
1. Create a boxplot that compares the color and clarity of the diamonds. Paste it here.
2. Describe the similarities and differences in the data sets. Please be specific to the graph created.
Part III. Finding Descriptive Numbers
Instructions
Answers
3. Open the file named Stowaway (using Datasets and then Elementary Stats, 9th Edition). This gives information on the number of stowaways going west vs east.List all the variables in the dataset.
4. Find the Mean, median, and midrange for the Data in Column 1.
5. Find the Range, variance, and standard deviation for the first column.
6. List any values for the first column that you think may be outliers. Why do you think that?
[Hint: You may want to sort the data and look at the smallest and largest values.]
7. Find the Mean, median, and midrange for the data in Column 2.
8. Find the Range, variance, and standard deviation for the data in Column 2.
9. List any values for the second column that you think may be outliers. Why do you think that?
10. Find the five-number summary for the stowaways data in Columns 1 and 2. You will need to label each of the columns with an appropriate measure in the top row for clarity.
11. Compare number of stowaways going west and east using a boxplot of Columns 1 and 2. Paste your boxplot here
12. Create a histogram for the
Column 1 data and paste it here.
13. Create a histogram for the
Column 2 data and paste it here.
Part IV. Interpreting Statistical Information
The Stowaway data contains two columns, both of which are mea.
Tech Tips (NLADA Substantive Law Conference 2010)
Tech Tips (NLADA Substantive Law Conference 2010)Legal Services National Technology Assistance Project (LSNTAP)
Presenters:
* Kathleen Brockel, NTAP
* Rachel Medina, NTAP
Contributors:
The tips represent the "best of" various tech tips contributed by past "Tech Tips" presenters including Glenn Rawdon (LSC), Molly French (Colorado Legal Services), Joyce Raby (Consultant), Matthew Burnett (Pro Bono Net), and David Bonebrake (NTAP).Lecture 2
The document provides an overview of key concepts in economics. It discusses how economics uses scientific methods and relies on positive and normative analysis. Two important models are introduced: the circular flow model which illustrates transactions between households and firms, and the production possibilities frontier which shows output combinations given available resources. Microeconomics focuses on individual parts of the economy while macroeconomics looks at aggregate trends.
Using Machine Learning to aid Journalism at the New York Times
This talk was presented to NYC Open Data Meetup Group on Nov 11, 2014.
Speaker:
Daeil Kim is currently a data scientist at the Times and is finishing up his Ph.D at Brown University on work related to developing scalable inference algorithms for Bayesian Nonparametric models. His work at the Times spans a variety of problems related to the company's business interests, audience development, as well as developing tools to aid journalism.
Topic:
This talk will focus mostly on how machine learning can help problems that prop up in journalism. We'll begin first by talking about using popular supervised learning algorithms such as regularized Logistic Regression to help assist a journalist's work in uncovering insights into a story regarding the recall of Takata airbags in cars. Afterwards, we'll think about using topic modeling to deal with large document dumps generated from FOIA (Freedom of Information Act) requests and Refinery, a simple web based tool to ease the implementation of such tasks. Finally, if there is time, we will go over how topic models have been extended to assist in the problem of designing an efficient recommendation engine for text-based content.
Data cogitate copart
Copart is a provider of online vehicle auction and remarketing services in the United States, Canada and United Kingdom. It provides vehicle sellers with a range of services to process and sell savage and clean title vehicles using its virtual auction technology. The challenge was to analyze their data and come up with useful recommendations for their business. We performed the following three analysis on the given dataset primarily using SAS, R and excel:
1. Time series analysis to forecast their revenue for the next year.
2. Copart's revenue model is built around both seller and buyer fees. We developed an optimization model to arrive at the optimum value of buyer fees at the given rate of seller fees, to achieve the target revenue in the future years.
3. We computed an alignment metric to help Copart balance their yard alignment across the different states in the USA to meet the rising demand for auto auction vehicles and at the same time maximize their profit.
Long costs SFLS
In the long run, all inputs including capital and labor can be varied as a firm changes its scale of production. As scale increases, a firm may experience economies of scale where average total cost decreases, constant returns to scale where average total cost remains the same, or diseconomies of scale where average total cost increases. The long-run average cost curve depicts the lowest attainable average total cost for a given output level as a firm adjusts its size.
Declarative Multilingual Information Extraction with SystemT
Information extraction (IE), the task of extracting structured information from unstructured or semi-structured data, is increasingly important to a wide array of enterprise applications, ranging from Business Intelligence to Data-as-a-Service.
In the first part of the talk, we give an overview of SystemT, a declarative IE system designed and developed to address the requirements driven by modern applications: scalability, expressivity, and transparency. SystemT is based on the basic principle underlying relational database technology: complete separation of specification from execution. SystemT uses a declarative language for expressing NLP algorithms called AQL, and an optimizer that generates high-performance algebraic execution plans for AQL rules. It makes IE orders of magnitude more scalable and easy to use, maintain and customize. Today, SystemT ships with multiple products across 4 IBM Software Brands and it being taught in universities. Our ongoing research and development efforts focus on making SystemT more usable for both technical and business users, and continuing enhancing its core functionalities based on natural language processing, machine learning, and database technology.
In the second part of the talk we present POLYGLOT, a multilingual semantic role labeling system capable of semantically parsing sentences in 9 different languages from 4 different language groups. The key feature of the system is that it treats the semantic labels of the English Proposition Bank as “universal semantic labels”: Given a sentence in any of the supported languages, POLYGLOT will predict appropriate English PropBank frame and role annotation. We illustrate how these universal semantic labels can be used within SystemT to create information extractors that immediately work across different languages. In addition, we illustrate how we automatically generate Proposition Banks for new languages in order to enable multilingual SRL and discuss some challenges of crosslingual semantics.
Ch. 2 - Thinking Like an Economist 1.ppt
The document provides an overview of key concepts in economics including:
- Economics trains people to think in terms of alternatives and costs of choices.
- Economists use descriptive and analytical approaches including developing models from theories.
- Economists serve as scientists when explaining the world using the scientific method and models, and as policymakers when trying to change the world.
- Models like the circular flow diagram and production possibilities frontier are used to simplify and explain economic concepts.
Tip: Data Scoring: Convert data with XQuery
The process of converting data is one of migrating information from an unsuitable
source or format to a suitable one—often not an exact science. Data scoring is a way
to measure the accuracy of your conversion. Discover a simple scoring technique in
XQuery that you can apply to the result of a small text-to-XML conversion.
Scoring converted data is all about analyzing the quality of the conversion. Quality
can mean different things, and converting data from a database carries with it
different problems than converting data from documents with more natural language.
The technique that this tip presents makes no assumptions: You can apply it to any
XML code of interest. To see the technique in practice, you will convert plain
text—not comma-separated files, but plain text from news items grabbed from the
Internet.
Frequently used acronyms
• HTML: Hypertext Markup Language
• W3C: World Wide Web Consortium
• URL: Uniform Resource Locator
• XML: Extensible Markup Language
• XSLT: Extensible Stylesheet Transformations
Jewelry
The document describes Del Mar Data Systems' jewelry inventory management software. The software allows jewelry distributors to track individual pieces of jewelry from receipt through sale using barcodes and digital photos. It maintains records of transactions and item details, locations, and statuses. Comprehensive reports can be generated on inventory, costs, transactions, and more. The software aims to help users efficiently manage their jewelry inventory and operations.
Graphic Representation Grading GuideCOMTM541 Version 22.docx
The document provides a grading guide for an assignment that requires students to create two graphs or tables from the same data set that tell different stories. The guide outlines the criteria that will be used to evaluate the students' work, including choosing an appropriate data source, creating graphs that illustrate opposing stories, comparing the results, and addressing ethical implications. It also provides guidelines for formatting, citations, grammar, and other writing conventions. The overall purpose is for students to think critically about how data can be represented in a way that tells different or misleading stories, and to analyze graphs by considering the motivation and perspective of their creators.
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Check out our proposed agenda below 👇👇
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Legal Language Explorer .com Tutorial
- 1. Legal Language Explorer A Brief Tutorial daniel martin katz michael j bommarito ii julie seaman adam candeub eugene agichtein
- 2. Go to LegalLanguageExplorer.com And Access This Interface
- 3. Notice The Default Search Is Interstate Commerce, Railroad, Deed
- 4. Notice The Default Search Is Interstate Commerce, Railroad, Deed
- 5. For Each Comma Separated Phrase The Frequency Plot Appears Below
- 6. For Each Comma Separated Phrase The Frequency Plot Appears Below
- 7. Currently Supporting Every U.S. Supreme Court Decision From 1791 - 2005
- 8. Currently Supporting Every U.S. Supreme Court Decision From 1791 - 2005
- 9. Years Can Be Changed By The End User
- 10. Years Can Be Changed By The End User
- 11. Click Here to Access Various Advanced Search Features
- 13. Are the Observed Trends a Function of changes in the volume of the case docket?
- 14. Normalization allows End User to control for the Size of the Case Docket
- 15. Normalization allows End User to control for the Size of the Case Docket
- 16. Change the Graph Type By Clicking Here
- 17. An Alternative Presentation of the Data
- 18. Export the Chart Data (If You Want to Replot in Stata, Excel, R, etc.)
- 19. Export the Chart Data (If You Want to Replot in Stata, Excel, R, etc.)
- 20. The Results (If You Want to Replot in Stata, Excel, R, etc.)
- 21. Access a Case List and Full Text Results
- 22. Here are the Returned Results
- 23. Download List in Excel
- 24. Access the Full Text a Particular Case
- 25. Click Through to Access Results from BulkResource
- 27. Stay Tuned for More in 2012 :) daniel martin katz michael j bommarito ii julie seaman adam candeub eugene agichtein