Evolutional Dependency Parse Trees for Biological Relation Extraction

2011 ◽  
Author(s):  
Hung-Yu Kao ◽  
Yi-Tsung Tang ◽  
Jian-Fu Wang
Keyword(s):  
Relation Extraction ◽  
Parse Trees ◽  
Biological Relation

scholarly journals Cross-lingual Adaptation Using Universal Dependencies

2021 ◽  
Vol 20 (4) ◽  
pp. 1-23
Author(s):  
Nasrin Taghizadeh ◽  
Heshaam Faili
Keyword(s):  
Case Studies ◽  
Relation Extraction ◽  
Low Resource ◽  
Parse Trees ◽  
Cross Lingual ◽  
Adaptation Method

We describe a cross-lingual adaptation method based on syntactic parse trees obtained from the Universal Dependencies (UD), which are consistent across languages, to develop classifiers in low-resource languages. The idea of UD parsing is to capture similarities as well as idiosyncrasies among typologically different languages. In this article, we show that models trained using UD parse trees for complex NLP tasks can characterize very different languages. We study two tasks of paraphrase identification and relation extraction as case studies. Based on UD parse trees, we develop several models using tree kernels and show that these models trained on the English dataset can correctly classify data of other languages, e.g., French, Farsi, and Arabic. The proposed approach opens up avenues for exploiting UD parsing in solving similar cross-lingual tasks, which is very useful for languages for which no labeled data is available.

scholarly journals Knowledge-Based Textual Inference via Parse-Tree Transformations

2015 ◽  
Vol 54 ◽  
pp. 1-57 ◽  
Author(s):  
Roy Bar-Haim ◽  
Ido Dagan ◽  
Jonathan Berant
Keyword(s):  
Natural Language ◽  
Relation Extraction ◽  
Practical Applications ◽  
Knowledge Based ◽  
Tree Transformations ◽  
Textual Entailment ◽  
Automatic Methods ◽  
Parse Trees ◽  
Recognizing Textual Entailment ◽  
Meaning Representation

Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as "external" to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe an inference formalism that operates directly on language-based structures, particularly syntactic parse trees. New trees are generated by applying inference rules, which provide a unified representation for varying types of inferences. We use manual and automatic methods to generate these rules, which cover generic linguistic structures as well as specific lexical-based inferences. We also present a novel packed data-structure and a corresponding inference algorithm that allows efficient implementation of this formalism. We proved the correctness of the new algorithm and established its efficiency analytically and empirically. The utility of our approach was illustrated on two tasks: unsupervised relation extraction from a large corpus, and the Recognizing Textual Entailment (RTE) benchmarks.

BioRelEx 1.0: Biological Relation Extraction Benchmark

2019 ◽  
Author(s):  
Hrant Khachatrian ◽  
Lilit Nersisyan ◽  
Karen Hambardzumyan ◽  
Tigran Galstyan ◽  
Anna Hakobyan ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Biological Relation

scholarly journals RelEx--Relation extraction using dependency parse trees

2006 ◽  
Vol 23 (3) ◽  
pp. 365-371 ◽  
Author(s):  
K. Fundel ◽  
R. Kuffner ◽  
R. Zimmer
Keyword(s):  
Relation Extraction ◽  
Parse Trees

DERIVING PATHWAY MAPS FROM AUTOMATED TEXT ANALYSIS USING A GRAMMAR-BASED APPROACH

2006 ◽  
Vol 04 (02) ◽  
pp. 483-501 ◽  
Author(s):  
BJÖRN OLSSON ◽  
BARBARA GAWRONSKA ◽  
BJÖRN ERLENDSSON
Keyword(s):  
Text Analysis ◽  
Large Scale ◽  
Relation Extraction ◽  
Syntactic Analysis ◽  
Epistemic Status ◽  
Regulatory Pathways ◽  
Semantic Class ◽  
Extraction Algorithm ◽  
Automated Text Analysis ◽  
Parse Trees

We demonstrate how automated text analysis can be used to support the large-scale analysis of metabolic and regulatory pathways by deriving pathway maps from textual descriptions found in the scientific literature. The main assumption is that correct syntactic analysis combined with domain-specific heuristics provides a good basis for relation extraction. Our method uses an algorithm that searches through the syntactic trees produced by a parser based on a Referent Grammar formalism, identifies relations mentioned in the sentence, and classifies them with respect to their semantic class and epistemic status (facts, counterfactuals, hypotheses). The semantic categories used in the classification are based on the relation set used in KEGG (Kyoto Encyclopedia of Genes and Genomes), so that pathway maps using KEGG notation can be automatically generated. We present the current version of the relation extraction algorithm and an evaluation based on a corpus of abstracts obtained from PubMed. The results indicate that the method is able to combine a reasonable coverage with high accuracy. We found that 61% of all sentences were parsed, and 97% of the parse trees were judged to be correct. The extraction algorithm was tested on a sample of 300 parse trees and was found to produce correct extractions in 90.5% of the cases.

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