For this assignment you will evaluate the performance of OpenCalais, a commercial entity extraction service, against your hand annotations.

1. Pick five random news stories and hand-annotate them. Pick an English-language news site with many stories on the home page, or a section of such a site (business, sports, etc.) Then generate five random numbers from 1 to the number of stories on the page. Cut and paste the text of each article into a separate  file, and save as plain text (no HTML, no formatting.)

2. Detect entities by hand in each article. Paste the text of each article into an RTF or Word document and go through it, underlining every entity. Count every mention of a person, place, or organization, including alternate names (“the president”) and pronoun references. Count how many entity references appear in each document (multiple mentions of the same entity all count.)

For this assignment you will analyze a social network using three different centrality algorithms, and compare the results.

1. Download and install Gephi, a free graph analysis package. It is open source and runs on any OS.

2. Download the data file lesmis.gml from the UCI Network Data Repository.  This is a network extracted from the famous French novel Les Miserables — you may also be familiar with the musical and the recent movie. Each node is a character, and there is an edge between two characters if they appear in the same chapter. Les Miserables is written in over 300 short chapters, so two characters that appear in the same chapter are very likely to meet or talk in the plot of the book. The edges are weighted, and the weight is the number of chapters those characters appear together in.

3. Open this file in Gephi, by choosing File->Open. When the dialog box comes up, set the “Graph Type” type to “Undirected.” The graph will be plotted. What do you see? Can you discern any patterns?

4. Now arrange the nodes in a nicer way, by choosing the “Force Atlas 2″ layout algorithm from the Layout menu at left and pressing the “Run” button. When things settle down, hit the “Stop” button. The graph will be arranged nicely, but it will be quite small.  You can zoom in using the mouse wheel (or two fingers on the trackpad on a mac) and pan using the right mouse button.

5. Posted in Assignment, Fall 2018

For this assignment you will design an information filtering algorithm. You will not implement it, but you will explain your design criteria and provide a filtering algorithm in sufficient technical detail to convince me that it might actually work — including psuedocode.

1. Decide who your users are. Journalists? Professionals? General consumers? Someone else?

2. Decide what you will filter. You can choose:

• Social network updates, like the Facebook news feed or Twitter trending topics
• A news organization recommendation engine
• The whole web, like Google News
• something else

3. List all available information that you have available as input to your algorithm. If you want to filter Facebook or Twitter, you may pretend that you are the company running the service, and have access to all posts and user data — from every user. You also also assume you have a web crawler or a firehose of every RSS feed or whatever you like, but you must be specific and realistic about what data you are operating with.

4. Argue for the design factors that you would like to influence the filtering, in terms of what is desirable to the user, what is desirable to the publisher (e.g. Facebook or a news organization), and what is desirable socially. Explain as concretely as possible how each of these (probably conflicting) goals might be achieved through in software. You can imagine that you have a UI that supports certain types of interactions (e.g. likes, votes, ratings) or encourages users to act in certain ways (e.g. following) that generate data for you.

5. Write psuedo-code for a function that produces a “top stories” list. This function will be called whenever the user loads your page or opens your app, so it must be fast and frequently updated. You can assume that there are background processes operating on your servers if you like. Your psuedo-code does not have to be executable, but it must be specific and unambiguous, such that I could actually go and implement it. You can assume that you have libraries for classic text analysis and machine learning algorithms. So, you don’t have to spell out algorithms like TF-IDF or item-based collaborative filtering, or anything else you can dig up in the research literature, but simply say how you’re going to use such building blocks. If you use an algorithm we haven’t discussed in class, be sure to provide a reference to it.

6. Write up steps 1-5. The result should be no more than three pages. You must be specific and plausible. You must be clear about what you are trying to accomplish, what your algorithm is, and why you believe your algorithm meets your design goals (though of course it’s impossible to know for sure without testing; but I want something that looks good enough to be worth trying.)

This assignment is designed to help you develop a feel for the way topic modeling works, the connection to the human meanings of documents, and one way of handling the time dimension. First you will analyze a corpus of AP news articles. Then you’ll look at the State of the Union speeches, and report on how the subjects have shifted over time in relation to historical events.

Note: This assignment requires reading the documents, not just running algorithms on them. I am asking you to tell me how well these algorithms capture the meaning of the documents, and you can only determine meaning by reading the documents. So when you see a word scored highly within a document set, go read the documents that contain that word. When a topic ranks high for a document, go read the documents that contain that topic.

1. Load the data. To begin with you’ll try LDA on a homogeneous document set of short clean articles: this collection of AP wire stories. Get this CSV loaded as a list of strings, one per document.

2) Generate TF-IDF vectors. Use the gensim package to generate tf-idf weighted document vectors. Check out the gensim example code here. You will need to go through the file twice: once to generate the dictionary (the code snippet starting with “collect statistics about all tokens”) and then again to convert each document to what gensim calls the bag-of-words representation, which is un-normalized term frequency (the code snippet starting with “class MyCorpus(object)”

Note that there is implicitly another step here, which is to tokenize the document text into individual word features — not as straightforward in practice as it seems at first, but the example code just does the simplest, stupidest thing, which is to lowercase the string and split on spaces. You may want to use a better stopword list, such as this one.

Once you have your Corpus object, tell gensim to generate tf-idf scores for you like so.

взять займ безработному. For this assignment you will evaluate the performance of OpenCalais, a commercial entity extraction service, against your hand annotations.

1. Pick five random news stories and hand-annotate them. Pick an English-language news site with many stories on the home page, or a section of such a site (business, sports, etc.) Then generate five random numbers from 1 to the number of stories on the page. Cut and paste the text of each article into a separate  file, and save as plain text (no HTML, no formatting.)

2. Detect entities by hand in each article. Paste the text of each article into an RTF or Word document and go through it, underlining every entity. Count every mention of a person, place, or organization, including alternate names (“the president”) and pronoun references. Count how many entity references appear in each document (multiple mentions of the same entity all count.)

3. Now run each document through OpenCalais. You can paste the text into the demo page here. Now count:

• Your hand count of entity references
• Correct entities: entities found by OC and marked by you
• Incorrect entities: entities found by OC but not marked by you
• Missed entities: entities marked by you but not found by OC

4. Turn in:

• Your hand-marked documents.
• A spreadsheet. Please turn in a spread sheet with one row per document, and four columns: hand-labelled, correct, incorrect, missed
• Make sure your spreadsheet includes totals of these four columns across all documents
• Your analysis. Report on any patterns in the failures that your see. Where is OpenCalais most accurate? Where is it least accurate? Are there predictable patterns to the errors? Are there ambiguities as to what is really an entity?

For this exercise, you will break into three groups. Two will analyze data, and the third will do legal research. The data for this assignment is available here.

All groups: How can we encode our ideas about racial fairness into a quantitative metric? That is the fundamental question underlying this assignment and you must answer it. Don’t build models that give you answers to useless questions; each model you build must embody some justifiable concept of fairness. Many such metrics have been proposed; part of the assignment is researching and evaluating them. I’ve proposed some models that might be interesting, but I’ll be just has happy — perhaps happier — to have you tell me why these particular model formulations will not yield an interesting result. Also, you have to tell me what your modeling results mean. Is there bias? In what way, how significant is it, and what are alternate explanations? Uninterpreted results will not get a passing grade.

Group 1: Analyze the stop and frisk data using a multi-level linear model

This group will analyze the data along the lines of Gelman 2006. However, that paper used Bayesian estimation whereas this team will used standard linear regression. Use RStudio with the lme4 package, as described in this tutorial.

As above, you need to choose and justify quantitative definitions of fairness. Should you look at stop rate? Hit rate? And should you compare to the racial composition of each precinct? Or per-race crime rates? And if so, which crimes?

Each conceptual definition of fairness can be embodied in many different statistical models. Compare at least two different statistical formulations. For example you might end up modeling:

• hit rate vs race, per precinct
• hit rate vs race, per precinct, with precinct random effects

You must also choose the unit of analysis. Is precinct or census block a better unit? Why? Or you could compare the same model on different units.

Your final post must include a justification of the metric and model choices, and useful visualization of the results, and interpretation of both the regression coefficients and uncertainty measures (standard errors on regression coefficients, and modeling residuals.)

For this assignment you will design a hybrid filtering algorithm. You will not implement it, but you will explain your design criteria and provide a filtering algorithm in sufficient technical detail to convince me that it might actually work — including psuedocode.

1. Decide who your users are. Journalists? Professionals? General consumers? Someone else?

2. Decide what you will filter. You can choose:

• Social network updates, like the Facebook news feed or Twitter trending topics
• A news organization recommendation engine
• The whole web, like Google News
• something else

3. List all available information that you have available as input to your algorithm. If you want to filter Facebook or Twitter, you may pretend that you are the company running the service, and have access to all posts and user data — from every user. You also also assume you have a web crawler or a firehose of every RSS feed or whatever you like, but you must be specific and realistic about what data you are operating with.

4. Argue for the design factors that you would like to influence the filtering, in terms of what is desirable to the user, what is desirable to the publisher (e.g. Facebook or a news organization), and what is desirable socially. Explain as concretely as possible how each of these (probably conflicting) goals might be achieved through in software. Since this is a hybrid filter, you can also design social software that asks the user for certain types of information (e.g. likes, votes, ratings) or encourages users to act in certain ways (e.g. following) that generate data for you.

займ без процентов на карту. This assignment is designed to help you develop a feel for the way topic modeling works, the connection to the human meanings of documents, and common ways of handling a time dimension. You will analyze the State of the Union speeches corpus, and report on how the subjects have shifted over time in relation to historical events.

1. Download the source data file state-of-the-union.csv. This is a standard CSV file with one speech per row. There are two columns: the year of the speech, and the text of the speech. You will write a Python program that reads this file and turns it into TF-IDF document vectors, then prints out some information. Here is how to read a CSV in Python. You may need to add the line

csv.field_size_limit(1000000000)

to the top of your program to be able to read this large file.

The file is a csv with columns year, text. Note: there are some years where there was more than one speech! Design your data structures accordingly.

2) Feed the data into gensim. Now you need to load the documents into Python and feed them into the gensim package to generate tf-idf weighted document vectors. Check out the gensim example code here. You will need to go through the file twice: once to generate the dictionary (the code snippet starting with “collect statistics about all tokens”) and then again to convert each document to what gensim calls the bag-of-words representation, which is un-normalized term frequency (the code snippet starting with “class MyCorpus(object)”