Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown semantic types using clustering methods on a vector space model of entity pairs and patterns. In this paper, we show that an informed feature generation technique based on dependency trees significantly improves clustering quality, as measured by the F-score, and therefore the ability of the URE metho...

Currently, large amounts of information exist in Web sites and various digital media. Most of them are in natural language. They are easy to be browsed, but difficult to be understood by computer. Chunk parsing and entity relation extracting is important work to understanding information semantic in natural language processing. Chunk analysis is a shallow parsing method, and entity relation ext...

This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules. We employ the proposed CORE techniques to extract more than 13 million entity-relations for an open domain question answering application....

When multiple ontologies are used within one application system, aligning the ontologies is a prerequisite for interoperability and unhampered semantic navigation and search. Various methods have been proposed to compute mappings between elements from different ontologies, the majority of which being based on various kinds of similarity measures. As a major shortcoming of these methods it is di...

Automated discovery and extraction of biological relations from online documents, particularly MEDLINE texts, has become essential and urgent because such literature data are accumulated in a tremendous growth. We present here an ontology-based framework of biological relation extraction system. This framework is unified and able to extract several kinds of relations such as gene-disease, gene-...

In the past decades, kernel methods are enthusiastically explored for relation extraction. This paper proposes a penalty tree similarity algorithm by extending the dependency tree kernel. Dependency tree kernel computes the similarity of two parse trees by enumerating their matched sub-trees. The penalty tree similarity, however, not only consider the similar structures of the parse trees, but ...

Relation extraction is a subtask of information extraction that aims at obtaining instances of semantic relations present in texts. This information can be arranged in machine-readable formats, useful for several applications that need structured semantic knowledge. The work presented in this paper explores different strategies to automate the extraction of semantic relations from texts in Port...

MOTIVATION The discovery of regulatory pathways, signal cascades, metabolic processes or disease models requires knowledge on individual relations like e.g. physical or regulatory interactions between genes and proteins. Most interactions mentioned in the free text of biomedical publications are not yet contained in structured databases. RESULTS We developed RelEx, an approach for relation ex...

One expensive step when defining crowdsourcing tasks is to define the examples and control questions for instructing the crowd workers. In this paper, we introduce a self-training strategy for crowdsourcing. The main idea is to use an automatic classifier, trained on weakly supervised data, to select examples associated with high confidence. These are used by our automatic agent to explain the ...

The ability to accurately determine temporal relations between events is an important task for several natural language processing applications such as Question Answering, Summarization, and Information Extraction. Since current supervised methods require large corpora, which for many languages do not exist, we have focused our attention on approaches with less supervision as much as possible. ...