Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand-crafted features and domain-specific resources which are difficult to collect and tune. For this reason, this paper investigates the effecti...

Detecting speculative assertions is essential to distinguish the facts from uncertain information for biomedical text. This paper describes a system to detect hedge cues and their scope using CRF model. HCDic feature is presented to improve the system performance of detecting hedge cues on BioScope corpus. The feature can make use of crossdomain resources.

In this paper we propose a non-Gibbsian Markov random field to model the spatial and topological relationships between objects in structured scenes. The field is formulated in terms of conditional probabilities learned from a set of training images. A locally consistent labelling of new scenes is achieved by relaxing the Markov random field directly using these conditional probabilities. We eva...

The paper presents a novel sentence trimmer in Japanese, which combines a non-statistical yet generic tree generation model and Conditional Random Fields (CRFs), to address improving the grammaticality of compression while retaining its relevance. Experiments found that the present approach outperforms in grammaticality and in relevance a dependency-centric approach (Oguro et al., 2000; Morooka...

Half of the world’s population is estimated to be at least bilingual. Due to this fact many people use multiple languages interchangeably for effective communication. At the Second Workshop on Computational Approaches to Code Switching, we are presented with a task to label codeswitched, Spanish-English (ES-EN) and Modern Standard Arabic-Dialect Arabic (MSA-DA), tweets. We built a Conditional R...

We describe a supervised system that uses optimized Conditional Random Fields and lexical features to predict the sentiment of a tweet. The system was submitted to the English version of all subtasks in SemEval-2017 Task 4.