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SemEval-2014 Task 4: Aspect Based Sentiment Analysis
TLDR
SemEval2014 Task 4 aimed to foster research in the field of aspect-based sentiment analysis, where the goal is to identify the aspects of given target entities and the sentiment expressed for each aspect.
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SemEval-2016 Task 5: Aspect Based Sentiment Analysis
TLDR
This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015, which attracted 245 submissions from 29 teams and provided 19 training and 20 testing datasets.
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An evaluation of Naive Bayesian anti-spam filtering
TLDR
It is reached that additional safety nets are needed for the Naive Bayesian anti-spam filter to be viable in practice.
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Natural language interfaces to databases – an introduction
TLDR
An introduction to natural language interfaces to databases (NLIDBS) and some less explored areas of NLIDB research are presented, namely database updates, meta-knowledge questions, temporal questions, and multi-modal NLIDBS.
View on Cambridge Press
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
TLDR
This work investigates thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks and compares it to an alternative memorybased learning approach, after introducing suitable cost-sensitive evaluation measures.
View PDF on arXiv
SemEval-2015 Task 12: Aspect Based Sentiment Analysis
TLDR
The task provided manually annotated reviews in three domains (restaurants, laptops and hotels), and a common evaluation procedure, to foster research beyond sentenceor text-level sentiment classification towards Aspect Based Sentiment Analysis.
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An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages
TLDR
This work introduces appropriate cost-sensitive measures, and investigates at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments.
View on ACM
Spam Filtering with Naive Bayes - Which Naive Bayes?
TLDR
An experimental procedure that emulates the incremental training of person- alized spam filters is adopted, and roc curves that allow us to compare the dierent versions of nb over the entire tradeo between true positives and true negatives are plotted.
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An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition
TLDR
Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.
View on Springer
LEGAL-BERT: The Muppets straight out of Law School
TLDR
This work proposes a broader hyper-parameter search space when fine-tuning for downstream tasks and releases LEGAL-BERT, a family of BERT models intended to assist legal NLP research, computational law, and legal technology applications.
View PDF on arXiv
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