Marius Pasca

IJCAI Conference 2015 Conference Paper

Dissecting German Grammar and Swiss Passports: Open-Domain Decomposition of Compositional Entries in Large-Scale Knowledge Repositories

• Marius Pasca
• Hylke Buisman

This paper presents a weakly supervised method that decomposes potentially compositional topics (Swiss passport) into zero or more constituent topics (Switzerland, Passport), where all topics are entries in a knowledge repository. The method increases the connectivity of the knowledge repository and, more importantly, identifies the constituent topics whose meaning can be later aggregated into the meaning of the compositional topics. By exploiting evidence within Wikipedia articles, the method acquires constituent topics of Freebase topics at precision and recall above 0. 60, over multiple human-annotated evaluation sets.

AAAI Conference 2008 Conference Paper

Turning Web Text and Search Queries into Factual Knowledge: Hierarchical Class Attribute Extraction

• Marius Pasca

A seed-based framework for textual information extraction allows for weakly supervised acquisition of open-domain class attributes over conceptual hierarchies, from a combination of Web documents and query logs. Automaticallyextracted labeled classes, consisting of a label (e. g. , painkillers) and an associated set of instances (e. g. , vicodin, oxycontin), are linked under existing conceptual hierarchies (e. g. , brain disorders and skin diseases are linked under the concepts BrainDisorder and SkinDisease respectively). Attributes extracted for the labeled classes are propagated upwards in the hierarchy, to determine the attributes of hierarchy concepts (e. g. , Disease) from the attributes of their subconcepts (e. g. , BrainDisorder and SkinDisease).

IJCAI Conference 2007 Conference Paper

• Marius Pasca
• Benjamin Van Durme

Within the larger area of automatic acquisition of knowledge from the Web, we introduce a method for extracting relevant attributes, or quantifiable properties, for various classes of objects. The method extracts attributes such as "capital city" and "President" for the class Country, or "cost, " "manufacturer" and "side effects" for the class Drug, without relying on any expensive language resources or complex processing tools. In a departure from previous approaches to large-scale information extraction, we explore the role of Web query logs, rather than Web documents, as an alternative source of class attributes. The quality of the extracted attributes recommends query logs as a valuable, albeit little explored, resource for information extraction.

AAAI Conference 2006 Conference Paper

Organizing and Searching the World Wide Web of Facts—Step One: The One-Million Fact Extraction Challenge

• Marius Pasca
• Jeffrey Bigham

Due to the inherent difficulty of processing noisy text, the potential of the Web as a decentralized repository of human knowledge remains largely untapped during Web search. The access to billions of binary relations among named entities would enable new search paradigms and alternative methods for presenting the search results. A first concrete step towards building large searchable repositories of factual knowledge is to derive such knowledge automatically at large scale from textual documents. Generalized contextual extraction patterns allow for fast iterative progression towards extracting one million facts of a given type (e. g. , Person-BornIn-Year) from 100 million Web documents of arbitrary quality. The extraction starts from as few as 10 seed facts, requires no additional input knowledge or annotated text, and emphasizes scale and coverage by avoiding the use of syntactic parsers, named entity recognizers, gazetteers, and similar text processing tools and resources.