This paper describes our participation in the closed track of the *SEM 2012 Shared Task of finding the scope of negation. To perform the task, we propose a system that has three components: negation cue detection, scope of negation detection, and negated event detection. In the first phase, the system creates a lexicon of negation signals from the training data and uses the lexicon to identify ...

Automatic detection of linguistic negation in free text is a critical need for many text processing applications, including sentiment analysis. This paper presents a negation detection system based on a conditional random field modeled using features from an English dependency parser. The scope of negation detection is limited to explicit rather than implied negations within single sentences. A...

Negation of clinical named entities is common in clinical documents and is a crucial factor to accurately compile patients' clinical conditions and to further support complex phenotype detection. In 2009, Mayo Clinic released the clinical Text Analysis and Knowledge Extraction System (cTAKES), which includes a negation annotator that identifies negation status of a named entity by searching for...

This paper presents an open-source toolkit for negation detection. It identifies negation cues and their corresponding scope in either raw or parsed text using maximummargin classification. The system design draws on best practice from the existing literature on negation detection, aiming for a simple and portable system that still achieves competitive performance. Pretrained models and experim...

This paper presents a survey on the role of negation in sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis. We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects, such as level of representation used for ...

In clinical practice guidelines (CPGs) the medical information is stored in a narrative way. A large part of this information occurs in a negated form. The detection of negation in CPGs is an important task since it helps medical personnel to identify not occurring symptoms and diseases as well as treatment actions that should not be accomplished. We developed algorithms capable of Negation Det...

In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system consists of two memory-based engines, one that decides if the tokens in a sentence are negation signals, and another that finds the full scope of the negation signals. Our approach to negation detection differs in two main aspects from existing research. First, we focus on finding...

Our paper presents the comparison of a machine-learnt and a manually constructed expert-rule-based biological event extraction system and some preliminary experiments to apply a negation and speculation detection system to further classify the extracted events. We report results on the BioNLP’09 Shared Task on Event Extraction evaluation datasets, and also on an external dataset for negation an...

An important part of information-gathering behaviour has always been to find out what other people think and whether they have favourable (positive) or unfavourable (negative) opinions about the subject. This survey studies the role of negation in an opinionoriented information-seeking system. We investigate the problem of determining the polarity of sentiments in movie reviews when negation wo...