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ICML / Peer Review 2013. Log in with a third-party provider. Use your Google account to log in. Log in with Google. Log in with a local account. You may create a local account if you prefer not to authenticate yourself through another provider. Create a new account by providing an email address, and we will send you instructions to complete the signup process. Send yourself an email to reset your password. Created by the Information Extraction and Synthesis Laboratory.
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ICML / Peer Review 2013. Log in with a third-party provider. Use your Google account to log in. Log in with Google. Log in with a local account. You may create a local account if you prefer not to authenticate yourself through another provider. Create a new account by providing an email address, and we will send you instructions to complete the signup process. Send yourself an email to reset your password. Created by the Information Extraction and Synthesis Laboratory.
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ICML / Peer Review 2013. Log in with a third-party provider. Use your Google account to log in. Log in with Google. Log in with a local account. You may create a local account if you prefer not to authenticate yourself through another provider. Create a new account by providing an email address, and we will send you instructions to complete the signup process. Send yourself an email to reset your password. Created by the Information Extraction and Synthesis Laboratory.
kedarbellare.github.io
Kedar Bellare > Home
http://kedarbellare.github.io/index.html
I am a graduate student pursuing my M.S/Ph.D in Computer Science. At University of Massachusetts, Amherst. UMass). Prior to UMass, I completed my undergraduate studies (B.Tech) at IIT. Currently, I am working in the Information Extraction and Synthesis Laboratory. My advisor is Prof. Andrew McCallum. I apply machine learning techniques to problems in information extraction and natural language processing.
factorie.cs.umass.edu
FACTORIE: Inference
http://factorie.cs.umass.edu/usersguide/UsersGuide450Inference.html
The basic use of a graphical model is to perform. Making predictions about the values of unobserved variables, conditioned on the values of observed variables and the parameters. In FACTORIE terminology, inference is a process which takes a list of variables and a model and produces a Summary, which is a container of marginals which also has a marginalization constant, referred to as logZ. The main traits involved in the process of inference in FACTORIE are Marginal, FactorMarginal, Summary, and Infer.
factorie.cs.umass.edu
FACTORIE: Installation
http://factorie.cs.umass.edu/usersguide/UsersGuide020Installation.html
FACTORIE is known to run on Mac OSX, Linux, and Windows with Cygwin, and is expected to run on any platform on which the Java Virtual Machine is available. If you encounter difficulties installing FACTORIE please let us know at discuss@factorie.cs.umass.edu. 17 and Apache Maven. 30 Maven will install the correct versions of all other dependencies for you, including Scala. Adding this jar to the classpath of your Java project will allow you to use FACTORIE in that project. Factorie 2.11 /artifactId. To co...
factorie.cs.umass.edu
FACTORIE: Introduction
http://factorie.cs.umass.edu/usersguide/UsersGuide010Introduction.html
Andrew McCallum, Alexandre Passos, Sameer Singh,. Is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating factor graphs. Estimating parameters and performing inference. FACTORIE aims to provide a full-featured framework for probabilistic graphical models (both directed and undirected) that is both flexible for rapid prototyping and efficient at large scale for deployment in substantial applications. It is ...
factorie.cs.umass.edu
FACTORIE: Publications and Sponsors
http://factorie.cs.umass.edu/pubs.html
Research related to FACTORIE is described in several publications. These include:. Andrew McCallum, Karl Schultz, Sameer Singh. FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs. In Advances on Neural Information Processing Systems (NIPS), 2009. Sameer Singh, Karl Schultz, Andrew McCallum. Bi-directional Joint Inference for Entity Resolution and Segmentation using Imperatively-Defined Factor Graphs. International Conference on Very Large Data Bases (VLDB), 2010. And is free to us...
factorie.cs.umass.edu
FACTORIE: Download
http://factorie.cs.umass.edu/download.html
The latest release of Factorie is 1.2. Executable Jar (2.11): factorie 2.11-1.2.jar. Source files (2.11): factorie 2.11-1.2-sources.jar. Or, you can can clone the latest snapshot version from GitHub:. Git clone https:/ github.com/factorie/factorie. To download earlier versions of Factorie (for example 1.0), see our GitHub releases page. Factorie is open source software Apache License 2.0. Information Extraction and Synthesis Laboratory. IESL), College of Information and Computer Sciences.
factorie.cs.umass.edu
FACTORIE: Learning
http://factorie.cs.umass.edu/usersguide/UsersGuide460Learning.html
In this tutorial we set up a simple linear chain CRF, such as the one used for part-of-speech tagging, named-entity recognition, or noun phrase chunking. It will be our running example in this tutorial, but most of the things we’ll discuss generalize all across Factorie. First we create domains representing the labels and features. The Document class implements documents as sequences of sentences and tokens. The quick brown fox jumped over the lazy dog.". Let's also initialize features for all tokens.
