66,668 research outputs found
Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity
In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system’s architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands)
Wrapper Maintenance: A Machine Learning Approach
The proliferation of online information sources has led to an increased use
of wrappers for extracting data from Web sources. While most of the previous
research has focused on quick and efficient generation of wrappers, the
development of tools for wrapper maintenance has received less attention. This
is an important research problem because Web sources often change in ways that
prevent the wrappers from extracting data correctly. We present an efficient
algorithm that learns structural information about data from positive examples
alone. We describe how this information can be used for two wrapper maintenance
applications: wrapper verification and reinduction. The wrapper verification
system detects when a wrapper is not extracting correct data, usually because
the Web source has changed its format. The reinduction algorithm automatically
recovers from changes in the Web source by identifying data on Web pages so
that a new wrapper may be generated for this source. To validate our approach,
we monitored 27 wrappers over a period of a year. The verification algorithm
correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes,
resulting in precision of 0.73 and recall of 0.95. We validated the reinduction
algorithm on ten Web sources. We were able to successfully reinduce the
wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data
extraction task
Enriching Knowledge Bases with Counting Quantifiers
Information extraction traditionally focuses on extracting relations between
identifiable entities, such as . Yet, texts
often also contain Counting information, stating that a subject is in a
specific relation with a number of objects, without mentioning the objects
themselves, for example, "California is divided into 58 counties". Such
counting quantifiers can help in a variety of tasks such as query answering or
knowledge base curation, but are neglected by prior work. This paper develops
the first full-fledged system for extracting counting information from text,
called CINEX. We employ distant supervision using fact counts from a knowledge
base as training seeds, and develop novel techniques for dealing with several
challenges: (i) non-maximal training seeds due to the incompleteness of
knowledge bases, (ii) sparse and skewed observations in text sources, and (iii)
high diversity of linguistic patterns. Experiments with five human-evaluated
relations show that CINEX can achieve 60% average precision for extracting
counting information. In a large-scale experiment, we demonstrate the potential
for knowledge base enrichment by applying CINEX to 2,474 frequent relations in
Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct
relations, which is 28% more than the existing Wikidata facts for these
relations.Comment: 16 pages, The 17th International Semantic Web Conference (ISWC 2018
PANGAEA information system for glaciological data management
Specific parameters determined on cores from continental ice sheets or glaciers can be used to reconstruct former climate. To use this scientific resource effectively an information system is needed which guarantees consistent longtime storage of data and provides easy access for the scientific community.An information system to archive any data of paleoclimatic relevance, together with the related metadata, raw data and evaluated paleoclimatic data, is presented. The system, based on a relational database, provides standardized import and export routines, easy access with uniform retrieval functions, and tools for the visualization of the data. The network is designed as a client/server system providing access through the Internet with proprietary client software including a high functionality or read-only access on published data via the World Wide Web
Harvesting Entities from the Web Using Unique Identifiers -- IBEX
In this paper we study the prevalence of unique entity identifiers on the
Web. These are, e.g., ISBNs (for books), GTINs (for commercial products), DOIs
(for documents), email addresses, and others. We show how these identifiers can
be harvested systematically from Web pages, and how they can be associated with
human-readable names for the entities at large scale.
Starting with a simple extraction of identifiers and names from Web pages, we
show how we can use the properties of unique identifiers to filter out noise
and clean up the extraction result on the entire corpus. The end result is a
database of millions of uniquely identified entities of different types, with
an accuracy of 73--96% and a very high coverage compared to existing knowledge
bases. We use this database to compute novel statistics on the presence of
products, people, and other entities on the Web.Comment: 30 pages, 5 figures, 9 tables. Complete technical report for A.
Talaika, J. A. Biega, A. Amarilli, and F. M. Suchanek. IBEX: Harvesting
Entities from the Web Using Unique Identifiers. WebDB workshop, 201
An analysis of the manuals of five fifth and sixth grade history textbook series.
Thesis (Ed.M.)--Boston Universit
Sharing Computer Network Logs for Security and Privacy: A Motivation for New Methodologies of Anonymization
Logs are one of the most fundamental resources to any security professional.
It is widely recognized by the government and industry that it is both
beneficial and desirable to share logs for the purpose of security research.
However, the sharing is not happening or not to the degree or magnitude that is
desired. Organizations are reluctant to share logs because of the risk of
exposing sensitive information to potential attackers. We believe this
reluctance remains high because current anonymization techniques are weak and
one-size-fits-all--or better put, one size tries to fit all. We must develop
standards and make anonymization available at varying levels, striking a
balance between privacy and utility. Organizations have different needs and
trust other organizations to different degrees. They must be able to map
multiple anonymization levels with defined risks to the trust levels they share
with (would-be) receivers. It is not until there are industry standards for
multiple levels of anonymization that we will be able to move forward and
achieve the goal of widespread sharing of logs for security researchers.Comment: 17 pages, 1 figur
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