Ana Popescu

Followers

Following

Public Views

Transitioned fields.
Previously: Consulting for SV startups (e.g., Pinterest), Yahoo! Labs (Applied Research)

less

Stanford University

Oregon State University

Suzanne Tamang

Stanford University

Michael Wick

University of Massachusetts Amherst

Ronen Feldman

The Hebrew University of Jerusalem

Dawn Lawrie

Loyola University Maryland

Interests

Uploads

Papers by Ana Popescu

Unsupervised named-entity extraction from the Web: An experimental study

Artificial Intelligence, 2005

The KNOWITALL system aims to automate the tedious process of extracting large collections of fact... more The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KNOW-ITALL's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KNOW-ITALL extracted over 50,000 class instances, but suggested a challenge: How can we improve KNOWITALL's recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., "chemist" and "biologist" are identified as sub-classes of "scientist"). List Extraction locates lists of class instances, learns a "wrapper" for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KNOWITALL a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.

Download

Web-Scale Distributional Similarity and Entity Set Expansion

Computing the pairwise semantic similarity between all words on the Web is a computationally chal... more Computing the pairwise semantic similarity between all words on the Web is a computationally challenging task. Parallelization and optimizations are necessary. We propose a highly scalable implementation based on distributional similarity, implemented in the MapReduce framework and deployed over a 200 billion word crawl of the Web. The pairwise similarity between 500 million terms is computed in 50 hours using 200 quad-core nodes. We apply the learned similarity matrix to the task of automatic set expansion and present a large empirical study to quantify the effect on expansion performance of corpus size, corpus quality, seed composition and seed size. We make public an experimental testbed for set expansion analysis that includes a large collection of diverse entity sets extracted from Wikipedia.

Download

Modern Natural Language Interfaces to Databases: Composing Statistical Parsing with Semantic Tractability

Natural Language Interfaces to Databases (NLIs) can benefit from the advances in statistical pars... more Natural Language Interfaces to Databases (NLIs) can benefit from the advances in statistical parsing over the last fifteen years or so. However, statistical parsers require training on a massive, labeled corpus, and manually creating such a corpus for each database is prohibitively expensive. To address this quandary, this paper reports on the PRECISE NLI, which uses a statistical parser as a "plug in". The paper shows how a strong semantic model coupled with "light re-training" enables PRECISE to overcome parser errors, and correctly map from parsed questions to the corresponding SQL queries. We discuss the issues in using statistical parsers to build database-independent NLIs, and report on experimental results with the benchmark ATIS data set where PRECISE achieves 94% accuracy.

Download

Methods for Domain-Independent Information Extraction from the Web: An Experimental Comparison

Our KNOWITALL system aims to automate the tedious process of extracting large collections of fact... more Our KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an autonomous, domain-independent, and scalable manner. In its first major run, KNOWITALL extracted over 50,000 facts with high precision, but suggested a challenge: How can we improve KNOWITALL's recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Rule Learning learns domain-specific extraction rules. Subclass Extraction automatically identifies sub-classes in order to boost recall. List Extraction locates lists of class instances, learns a "wrapper" for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL's domain-independent methods, no hand-labeled training examples are required. Experiments show the relative coverage of each method and demonstrate their synergy. In concert, our methods gave KNOWITALL a 4-fold to 19-fold increase in recall, while maintaining high precision, and discovered 10,300 cities missing from the Tipster Gazetteer.

Download

Semantic lexicon adaptation for use in query interpretation

Page 1. Semantic Lexicon Adaptation for Use in Query Interpretation Ana-Maria Popescu Yahoo! Labs... more

Towards intent-driven bidterm suggestion

In online advertising, pervasive in commercial search engines, advertisers typically bid on few t... more In online advertising, pervasive in commercial search engines, advertisers typically bid on few terms, and the scarcity of data makes ad matching difficult. Suggesting additional bidterms can significantly improve ad clickability and conversion rates. In this paper, we present a large-scale bidterm suggestion system that models an advertiser's intent and finds new bidterms consistent with that intent. Preliminary experiments show that our system significantly increases the coverage of a state of the art production system used at Yahoo while maintaining comparable precision.

Download

Unsupervised named-entity extraction from the Web: An experimental study

Artificial Intelligence, 2005

Download

Web-Scale Distributional Similarity and Entity Set Expansion

Download

Modern Natural Language Interfaces to Databases: Composing Statistical Parsing with Semantic Tractability

Download

Methods for Domain-Independent Information Extraction from the Web: An Experimental Comparison

Download

Semantic lexicon adaptation for use in query interpretation

Page 1. Semantic Lexicon Adaptation for Use in Query Interpretation Ana-Maria Popescu Yahoo! Labs... more

Towards intent-driven bidterm suggestion

Download

Ana Popescu

Uploads

Papers by Ana Popescu

Log In