• Publications
  • Influence
SemEval-2014 Task 4: Aspect Based Sentiment Analysis
TLDR
Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. Expand
  • 699
  • 107
  • PDF
An evaluation of Naive Bayesian anti-spam filtering
TLDR
It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail (“spam”). Expand
  • 649
  • 66
  • PDF
Natural language interfaces to databases - an introduction
TLDR
This paper is an introduction to natural language interfaces to databases (NLIDBS) and reflections on the current state of the art. Expand
  • 758
  • 58
  • PDF
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
TLDR
We investigate the performance of two machine learning algorithms in the context of antispam filtering. Expand
  • 392
  • 44
  • PDF
An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages
TLDR
The growing problem of unsolicited bulk e-mails, also known as “spam”, has generated a need for reliable anti-spam e-mail filters. Expand
  • 515
  • 43
  • PDF
Spam Filtering with Naive Bayes - Which Naive Bayes?
TLDR
We discuss five dierent versions of Naive Bayes, and compare them on six new, non-encoded datasets, that contain ham messages of particular Enron users and fresh spam messages and emulate the varying proportion of spam and ham messages that users receive over time. Expand
  • 508
  • 36
  • PDF
SemEval-2015 Task 12: 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. Expand
  • 345
  • 30
  • PDF
Learning to Filter Unsolicited Commercial E-Mail
TLDR
We present a thorough investigation on using machine learning to construct effective personalized anti-spam filters, and present an analysis of its behavior in real use. Expand
  • 170
  • 26
  • PDF
A Survey of Paraphrasing and Textual Entailment Methods
TLDR
Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Expand
  • 405
  • 21
  • PDF
SemEval-2015 Task 12: Aspect Based Sentiment Analysis
TLDR
SemEval-2015 Task 12, a continuation of Sem Eval-2014 Task 4, aimed to foster research beyond sentenceor text-level sentiment classification towards Aspect Based Sentiment Analysis. Expand
  • 172
  • 20
  • PDF