Negation scope detection with a conditional random field model

Identifying negation cues and their scope in a text is an important subtask of information extraction that can benefit other natural language processing tasks,including but not limited to medical data mining,relation extraction,question answering and sentiment analysis.The tasks of negation cue and negation scope detection can be treated as sequence labelling problems.In this paper,a system is presented having two components:negation cue detection and negation scope detection.In the first phase,a conditional random field(CRF) model is trained to detect the negation cues using a lexicon of negation words and some lexical and contextual features.Then,another CRF model is trained to detect the scope of each negation cue identified in the first phase,using basic lexical and contextual features.These two models are trained and tested using the dataset distributed within the* Sem Shared Task 2012 on resolving the scope and focus of negation.Experimental results show that the system outperformed all the systems submitted to this shared task.

Negation Scope Detection with Recurrent Neural Networks Models in Review Texts

Identifying negation scopes in a text is an important subtask of information extraction, that can benefit other natural language processing tasks, like relation extraction, question answering and sentiment analysis. And serves the task of social media text understanding. The task of negation scope detection can be regarded as a token-level sequence labeling problem. In this paper, we propose different models based on recurrent neural networks (RNNs) and word embedding that can be successfully applied to such tasks without any task-specific feature engineering efforts. Our experimental results show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based model.

A syntactic path-based hybrid neural network for negation scope detection

The automatic detection of negation is a crucial task in a wide-range of natural language processing(NLP)applications,including medical data mining,relation extraction,question answering,and sentiment analysis.In this paper,we present a syntactic path-based hybrid neural network architecture,a novel approach to identify the scope of negation in a sentence.Our hybrid architecture has the particularity to capture salient information to determine whether a token is in the scope or not,without relying on any human intervention.This approach combines a bidirectional long shortterm memory(Bi-LSTM)network and a convolutional neural network(CNN).The CNN model captures relevant syntactic features between the token and the cue within the shortest syntactic path in both constituency and dependency parse trees.The Bi-LSTM learns the context representation along the sentence in both forward and backward directions.We evaluate our model on the Bioscope corpus,and get 90.82%F-score(78.31%PCS)on the abstract sub-corpus,outperforming features-dependent approaches.