This paper describes experiments in classifying sentences of medical abstracts into a number of semantic classes given by section headings in structured abstracts. Using conditional random fields, we obtain F -scores ranging from 0.72 to 0.97. By using a small set of sentences that appear under the PARTICPANTS heading, we demonstrate that it is possible to recognize sentences that describe popu...

In this paper, we transform the issue of Chinese-English tense conversion into the issue of tagging a Chinese tense tree. And then we propose Markov Tree Tagging Model to tag nodes of the untagged tense tree with English tenses. Experimental results show that the method is much better than linear-based CRF tagging for the issue.

Conditional Random Fields (CRF) are among the most popular techniques for image labelling because of their flexibility in modelling dependencies between the labels and the image features. This paper proposes a novel CRFframework for image labeling problems which is capable to classify partially occluded objects. Our approach is evaluated on aerial near-vertical images as well as on urban street...

Recent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). We pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. We view this problem as an information extraction task and adopt a hybrid approach that combines Conditional Random Fields (Lafferty et al.,...

Training higher-order conditional random fields is prohibitive for huge tag sets. We present an approximated conditional random field using coarse-to-fine decoding and early updating. We show that our implementation yields fast and accurate morphological taggers across six languages with different morphological properties and that across languages higher-order models give significant improvemen...

Sentence segmentation is a fundamental issue in Classical Chinese language processing. To facilitate reading and processing of the raw Classical Chinese data, we propose a statistical method to split unstructured Classical Chinese text into smaller pieces such as sentences and clauses. The segmenter based on the conditional random field (CRF) model is tested under different tagging schemes and ...

We use Latent-Dynamic Conditional Random Fields to perform skeleton-based pointing gesture classification at each time instance of a video sequence, where we achieve a frame-wise pointing accuracy of roughly 83%. Subsequently, we determine continuous time sequences of arbitrary length that form individual pointing gestures and this way reliably detect pointing gestures at a false positive detec...

This work presents an English-to-Chinese (E2C) machine transliteration system based on two-stage conditional random fields (CRF) models with accessor variety (AV) as an additional feature to approximate local context of the source language. Experiment results show that two-stage CRF method outperforms the one-stage opponent since the former costs less to encode more features and finer grained l...

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.