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Found 13 papers, 2 papers with code
Stance Detection and Open Research Avenues
This tutorial aims to cover the state-of-the-art on stance detection and address open research avenues for interested researchers and practitioners.
Monitoring Energy Trends through Automatic Information Extraction
Energy research is of crucial public importance but the use of computer science technologies like automatic text processing and data management for the energy domain is still rare.
Stance Quantification: Definition of the Problem
At the end of the stance quantification process, a triple is obtained which consists of the percentages of the number of text items classified as Favor, Against, Neither, respectively, towards the target in the input pair.
To What Extent are Name Variants Used as Named Entities in Turkish Tweets?
We also provide finer-grained annotations of the named entities as well-formed names and different categories of name variants, where these annotations are made publicly-available.
A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection
Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions.
Stance Detection on Tweets: An SVM-based Approach
The results indicate that joint use of the features based on unigrams, hashtags, and named entities by SVM classifiers is a plausible approach for stance detection problem on sports-related tweets.
OntoWind: An Improved and Extended Wind Energy Ontology
Ontologies are critical sources of semantic information for many application domains.
Joint Named Entity Recognition and Stance Detection in Tweets
In this study, we investigate the possible contribution of named entities to the stance detection task in tweets.
Stance Detection in Turkish Tweets
Stance detection is a classification problem in natural language processing where for a text and target pair, a class result from the set {Favor, Against, Neither} is expected.
On TimeML-Compliant Temporal Expression Extraction in Turkish
It is commonly acknowledged that temporal expression extractors are important components of larger natural language processing systems like information retrieval and question answering systems.
A Knowledge-poor Pronoun Resolution System for Turkish
A pronoun resolution system which requires limited syntactic knowledge to identify the antecedents of personal and reflexive pronouns in Turkish is presented.
Experiments to Improve Named Entity Recognition on Turkish Tweets
In these experiments, starting with a baseline named entity recognition system, we adapt its recognition rules and resources to better fit Twitter language by relaxing its capitalization constraint and by diacritics-based expansion of its lexical resources, and we employ a simplistic normalization scheme on tweets to observe the effects of these on the overall named entity recognition performance on Turkish tweets.
Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles
The current study is significant as, to the best of our knowledge, it proposes the first considerably wide-coverage ontology for the wind energy domain and the ontology is built through a semi-automatic process which makes use of the related Web resources, thereby reducing the overall cost of the ontology building process.
