Michael J Cafarella, Age 45306 Arbana Dr, Ann Arbor, MI 48103

Michael Cafarella Phones & Addresses

306 Arbana Dr, Ann Arbor, MI 48103 (734) 929-4663

Westwood, MA

Seattle, WA

132 Oxford St, Cambridge, MA 02140 (617) 491-4011

132 Oxford St, Cambridge, MA 02140 (617) 945-0026

San Francisco, CA

Mountain View, CA

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Work

Address: 7 Forest St, Attleboro, MA 02703 Specialities: Psychologist

Languages

English

Mentions for Michael J Cafarella

Professional Records

Medicine Doctors

Michael Cafarella Photo 1

Michael J Cafarella, Attleboro MA - MA (Medicare Advantage)

Specialties:
Counseling
Address:
7 Forest St, Attleboro, MA 02703
(508) 222-5817 (Phone) (508) 223-4132 (Fax)
Languages:
English
Michael Cafarella Photo 2

Michael J Cafarella, Attleboro MA

Specialties:
Psychologist
Address:
7 Forest St, Attleboro, MA 02703

Resumes

Resumes

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Prosthodontist At Integrated Aesthetic Dentistry, Pllc

Position:
Prosthodontist, Owner at Integrated Aesthetic Dentistry, PLLC, Associate Prosthodontist at Specialized Dentistry New York
Location:
New York, New York
Industry:
Medical Practice
Work:
Integrated Aesthetic Dentistry, PLLC - 131 Macdougal Street, New York City since Jun 2009
Prosthodontist, Owner
Specialized Dentistry New York - 150 E 58th Street, Suite 3200 since Mar 2010
Associate Prosthodontist
Education:
Boston College 1996 - 2000
BA, Psychology
Tufts University School of Dental Medicine 2001 - 2005
DMD, Dentistry
Montefiore Medical Center 2005 - 2009
Specialty Certificate, Prosthodontics
Columbia University College of Dental Medicine 2010 - 2014
Certificate, Implantology Fellowship Program
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Senior Sales At Interline Brands

Position:
Senior Sales at Interline Brands
Location:
Greater Boston Area
Industry:
Wholesale
Work:
Interline Brands
Senior Sales
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Michael Cafarella

Location:
United States
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Michael Cafarella

Location:
United States

Publications

Us Patents

Open Information Extraction From The Web

US Patent:
7877343, Jan 25, 2011
Filed:
Apr 2, 2007
Appl. No.:
11/695506
Inventors:
Michael J. Cafarella - Seattle WA,
Michele Banko - Seattle WA,
Oren Etzioni - Seattle WA,
Assignee:
University of Washington through its Center for Commercialization - Seattle WA
International Classification:
G06E 1/00
G06F 15/18
US Classification:
706 20, 705 5, 382104
Abstract:
To implement open information extraction, a new extraction paradigm has been developed in which a system makes a single data-driven pass over a corpus of text, extracting a large set of relational tuples without requiring any human input. Using training data, a Self-Supervised Learner employs a parser and heuristics to determine criteria that will be used by an extraction classifier (or other ranking model) for evaluating the trustworthiness of candidate tuples that have been extracted from the corpus of text, by applying heuristics to the corpus of text. The classifier retains tuples with a sufficiently high probability of being trustworthy. A redundancy-based assessor assigns a probability to each retained tuple to indicate a likelihood that the retained tuple is an actual instance of a relationship between a plurality of objects comprising the retained tuple. The retained tuples comprise an extraction graph that can be queried for information.

Open Information Extraction From The Web

US Patent:
2011019, Aug 4, 2011
Filed:
Dec 16, 2010
Appl. No.:
12/970155
Inventors:
Michael J. Cafarella - Seattle WA,
Michele Banko - Seattle WA,
Oren Etzioni - Seattle WA,
Assignee:
University of Washington through its Center for Commercialization - Seattle WA
International Classification:
G06F 15/18
G06F 17/30
US Classification:
706 12, 707769, 707723, 707E17014, 707E17045
Abstract:
To implement open information extraction, a new extraction paradigm has been developed in which a system makes a single data-driven pass over a corpus of text, extracting a large set of relational tuples without requiring any human input. Using training data, a Self-Supervised Learner employs a parser and heuristics to determine criteria that will be used by an extraction classifier (or other ranking model) for evaluating the trustworthiness of candidate tuples that have been extracted from the corpus of text, by applying heuristics to the corpus of text. The classifier retains tuples with a sufficiently high probability of being trustworthy. A redundancy-based assessor assigns a probability to each retained tuple to indicate a likelihood that the retained tuple is an actual instance of a relationship between a plurality of objects comprising the retained tuple. The retained tuples comprise an extraction graph that can be queried for information.

Open Information Extraction

US Patent:
2014003, Jan 30, 2014
Filed:
Jul 26, 2013
Appl. No.:
13/952468
Inventors:
Michael Cafarella - Ann Arbor MI,
Michele Banko - Seattle WA,
International Classification:
G06F 17/27
US Classification:
704 9
Abstract:
A system for identifying relational tuples is provided. The system extracts a relation phrase from a sentence by identifying a verb in the sentence and then identifying a relation phrase of the sentence as a phrase in the sentence starting with the identified verb that satisfies both a syntactic constraint and a lexical constraint. The system also identifies arguments for a relation phrase. To extract the arguments, the system applies a left-argument-left-bound classifier, a left-argument-right-bound classifier, and a right-argument-right-bound classifier to identify a left argument and right argument for the relation phrase such that the left argument, the relation phrase, and the right argument form a relational tuple.

Amazon

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This Is Not Available 043170

Author:
Michael John Cafarella
Publisher:
ProQuest, UMI Dissertation Publishing
Publication Date:
2011-09-11
Binding:
Paperback
Pages:
162
ISBN #:
1244096989
EAN Code:
9781244096981
This book is not available.

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