Sebastian Krause

Sebastian Krause
Deutsches Forschungszentrum für Künstliche Intelligenz | DFKI · Language Technology

Researcher

About

22
Publications
2,656
Reads
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228
Citations
Additional affiliations
July 2016 - December 2016
Google Inc.
Position
  • Intern
April 2014 - September 2014
Google Inc.
Position
  • Intern
December 2012 - present
Deutsches Forschungszentrum für Künstliche Intelligenz
Position
  • PhD Student
Education
October 2006 - February 2012
Humboldt-Universität zu Berlin
Field of study
  • Computer Science

Publications

Publications (22)
Article
Accurate prediction of suitable discourse connectives (however, furthermore, etc.) is a key component of any system aimed at building coherent and fluent discourses from shorter sentences and passages. As an example, a dialog system might assemble a long and informative answer by sampling passages extracted from different documents retrieved from t...
Article
Full-text available
In relation extraction, a key process is to obtain good detectors that find relevant sentences describing the target relation. To minimize the necessity of labeled data for refining detectors, previous work successfully made use of BabelNet, a semantic graph structure expressing relationships between synsets, as side information or prior knowledge....
Article
Recent years have seen a significant growth and increased usage of large-scale knowledge resources in both academic research and industry. We can distinguish two main types of knowledge resources: those that store factual information about entities in the form of semantic relations (e.g., Freebase), namely so-called knowledge graphs, and those that...
Conference Paper
Full-text available
Recent research shows the importance of linking linguistic knowledge resources for the creation of large-scale linguistic data. We describe our approach for combining two English resources, FrameNet and sar-graphs, and illustrate the benefits of the linked data in a relation extraction setting. While FrameNet consists of schematic representations o...
Conference Paper
The task of relation extraction is to recognize and extract relations between entities or concepts in texts. Dependency parse trees have become a popular source for discovering extraction patterns, which encode the grammatical relations among the phrases that jointly express relation instances. State-of-the-art weakly supervised approaches to relat...
Conference Paper
Full-text available
Coreference resolution for event mentions enables extraction systems to process document-level information. Current systems in this area base their decisions on rich semantic features from various knowledge bases, thus restricting them to domains where such external sources are available. We propose a model for this task which does not rely on such...
Conference Paper
Full-text available
Patterns extracted from dependency parses of sentences are a major source of knowledge for most state-of-the-art relation extraction systems, but can be of low quality in distantly supervised settings. We present a linguistic annotation tool that allows human experts to analyze and categorize automatically learned patterns, and to identify common e...
Conference Paper
Full-text available
This paper describes IDEST, a new method for learning paraphrases of event patterns. It is based on a new neural network architecture that only relies on the weak supervision signal that comes from the news published on the same day and mention the same real-world entities. It can generalize across extractions from different dates to produce a robu...
Conference Paper
Full-text available
A new method is proposed and evaluated that improves distantly supervised learning of pattern rules for n-ary relation extraction. The new method employs knowledge from a large lexical semantic repository to guide the discovery of patterns in parsed relation mentions. It extends the induced rules to semantically relevant material outside the minima...
Conference Paper
Full-text available
In this paper, we present a novel combination of two types of language resources dedicated to the detection of relevant relations (RE) such as events or facts across sentence boundaries. One of the two resources is the sar-graph, which aggregates for each target relation ten thousands of linguistic patterns of semantically associated relations that...
Conference Paper
Full-text available
This paper presents a new resource for the training and evaluation needed by relation extraction experiments. The corpus consists of annotations of mentions for three semantic relations: marriage, parent–child, siblings, selected from the domain of biographic facts about persons and their social relationships. The corpus contains more than one hund...
Article
The article demonstrates how generic parsers in a minimally supervised information extraction framework can be adapted to a given task and domain for relation extraction (RE). For the experiments, two parsers that deliver n-best readings are included: (1) a generic deep-linguistic parser (PET) with a largely hand-crafted head-driven phrase structur...
Conference Paper
Full-text available
Web-scale relation extraction is a means for building and extending large repositories of formalized knowledge. This type of automated knowledge building requires a decent level of precision, which is hard to achieve with automatically acquired rule sets learned from unlabeled data by means of distant or minimal supervision. This paper shows how pr...
Conference Paper
Full-text available
We present a large-scale domain-adaptive relation extraction (RE) system, which learns grammar-based RE rules from the Web by utilizing large numbers of known relation instances as seed. The system does not only detect binary but also nary relations such as events. Our goal is to discover rule sets large enough for the actual range of linguistic va...
Conference Paper
Full-text available
The paper demonstrates how the generic parser of a minimally supervised information extraction framework can be adapted to a given task and domain for relation extraction (RE). For the experiments a generic deep-linguistic parser was employed that works with a largely hand-crafted head-driven phrase structure grammar (HPSG) for English. The output...
Conference Paper
Full-text available
This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some limited closed-world knowledge for confidence estimation of learned rules to the usual seed data, the precision of relation extraction can be considerably improved. Starting from an existing baseline system we demonstrate that...

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