NLIRE: A natural language inference method for relation extraction

2021 ◽  
pp. 100686
Author(s):  
Wenfei Hu ◽  
Lu Liu ◽  
Yupeng Sun ◽  
Yu Wu ◽  
Zhicheng Liu ◽  
...  
Keyword(s):  
Natural Language ◽  
Relation Extraction ◽  
Inference Method

scholarly journals Relational Learning for Spatial Relation Extraction from Natural Language

2012 ◽  
pp. 204-220 ◽  
Author(s):  
Parisa Kordjamshidi ◽  
Paolo Frasconi ◽  
Martijn Van Otterlo ◽  
Marie-Francine Moens ◽  
Luc De Raedt
Keyword(s):  
Natural Language ◽  
Relational Learning ◽  
Relation Extraction ◽  
Spatial Relation

A Generalized Inference Method for Fuzzy Quantified and Truth-Qualified Natural Language Propositions

2007 ◽  
Vol 11 (5) ◽  
pp. 502-510 ◽  
Author(s):  
Wataru Okamoto ◽  
◽  
Shun’ichi Tano ◽  
Atsushi Inoue ◽  
Ryosuke Fujioka ◽  
...  
Keyword(s):  
Natural Language ◽  
Inference Method ◽  
Generalized Inference ◽  
Language Communication ◽  
Natural Language Communication

We propose a generalized inference method for constructing natural language communication. The method is used to obtain fuzzy quantifierQ’  when “QAareFis τ →Q’(m’A) aremFism’’τ” is inferred (Q, Q’: fuzzy quantifiers,A: fuzzy subject,m, m’, m’’: modifiers,F: fuzzy predicate, τ: truth qualifier). We show thatQ’  is resolved step by step for a non-increasing type (few, ...) and a non-decreasing type (most, ...).

An Inference Method for Fuzzy Quantified Natural Language Propositions Based on New Interpretation of Truth Qualification

2007 ◽  
Vol 11 (1) ◽  
pp. 71-78 ◽  
Author(s):  
Wataru Okamoto ◽  
◽  
Shun’ichi Tano ◽  
Toshiharu Iwatani ◽  
Atsushi Inoue ◽  
...  
Keyword(s):  
Natural Language ◽  
Fuzzy Sets ◽  
Inference Method ◽  
New Interpretation

In this paper, we propose a method that affects inference results leading to a new interpretation of a truth qualification by adding a weight attribute to truth qualified fuzzy sets. With this method, we can obtain different inference results depending on the truth qualifiers by transforming a statement with fuzzy quantified and truth qualified natural language propositions. We applied our method to four examples transforming a fuzzy predicate of the natural language propositions and showed an effectiveness of the method.

A NEW SHALLOW SEMANTIC PARSER FOR DESCRIBING THE CONCEPT STRUCTURE OF TEXT

2009 ◽  
Vol 03 (01) ◽  
pp. 131-149
Author(s):  
YULAN YAN ◽  
YUTAKA MATSUO ◽  
MITSURU ISHIZUKA
Keyword(s):  
Natural Language ◽  
Argument Structure ◽  
Semantic Annotation ◽  
Relation Extraction ◽  
Extraction Process ◽  
Preliminary Evaluation ◽  
Semantic Role Labeling ◽  
Thematic Relations ◽  
Relation Classification ◽  
Predicate Argument Structure

Recently, Semantic Role Labeling (SRL) systems have been used to examine a semantic predicate-argument structure for natural occurring texts. Facing the challenge of extracting a universal set of semantic or thematic relations covering various types of semantic relationships between entities, based on the Concept Description Language for Natural Language (CDL.nl) which defines a set of semantic relations to describe the concept structure of text, we develop a shallow semantic parser to add a new layer of semantic annotation of natural language sentences as an extension of SRL. The parsing task is a relation extraction process with two steps: relation detection and relation classification. Firstly, based on dependency analysis, a rule-based algorithm is presented to detect all entity pairs between each pair for which there exists a relationship; secondly, we use a kernel-based method to assign CDL.nl relations to detected entity pairs by leveraging diverse features. Preliminary evaluation on a manual dataset shows that CDL.nl relations can be extracted with good performance.

scholarly journals Using distant supervision to augment manually annotated data for relation extraction

2019 ◽  
Author(s):  
Peng Su ◽  
Gang Li ◽  
Cathy Wu ◽  
K. Vijay-Shanker
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Training Data ◽  
Distant Supervision ◽  
Large Size ◽  
Domain Expertise

AbstractSignificant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.

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