On the linguistic representational power of neural machine translation models
Despite the recent success of deep neural networks in natural language processing and
other spheres of artificial intelligence, their interpretability remains a challenge. We analyze …
other spheres of artificial intelligence, their interpretability remains a challenge. We analyze …
Negation and speculation in NLP: a Survey, Corpora, methods, and applications
Negation and speculation are universal linguistic phenomena that affect the performance of
Natural Language Processing (NLP) applications, such as those for opinion mining and …
Natural Language Processing (NLP) applications, such as those for opinion mining and …
NegBERT: a transfer learning approach for negation detection and scope resolution
A Khandelwal, S Sawant - arXiv preprint arXiv:1911.04211, 2019 - arxiv.org
Negation is an important characteristic of language, and a major component of information
extraction from text. This subtask is of considerable importance to the biomedical domain …
extraction from text. This subtask is of considerable importance to the biomedical domain …
[PDF][PDF] Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach
Detecting negation and uncertainty is crucial for medical text mining applications; otherwise,
extracted information can be incorrectly identified as real or factual events. Although several …
extracted information can be incorrectly identified as real or factual events. Although several …
[HTML][HTML] Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction
Abstract Background and Objectives: Named Entity Recognition (NER) and Relation
Extraction (RE) are two of the most studied tasks in biomedical Natural Language …
Extraction (RE) are two of the most studied tasks in biomedical Natural Language …
How does BERT's attention change when you fine-tune? An analysis methodology and a case study in negation scope
Large pretrained language models like BERT, after fine-tuning to a downstream task, have
achieved high performance on a variety of NLP problems. Yet explaining their decisions is …
achieved high performance on a variety of NLP problems. Yet explaining their decisions is …
A machine‐learning approach to negation and speculation detection for sentiment analysis
Recognizing negative and speculative information is highly relevant for sentiment analysis.
This paper presents a machine‐learning approach to automatically detect this kind of …
This paper presents a machine‐learning approach to automatically detect this kind of …
SFU ReviewSP-NEG: a Spanish corpus annotated with negation for sentiment analysis. A typology of negation patterns
In this paper, we present SFU Review SP-NEG, the first Spanish corpus annotated with
negation with a wide coverage freely available. We describe the methodology applied in the …
negation with a wide coverage freely available. We describe the methodology applied in the …
Deep learning approach for negation handling in sentiment analysis
PK Singh, S Paul - IEEE Access, 2021 - ieeexplore.ieee.org
Negation handling is an important sub-task in Sentiment Analysis. Negation plays a
significant role in written text. Negation terms in sentence often changes the polarity of entire …
significant role in written text. Negation terms in sentence often changes the polarity of entire …
Learning disentangled representations of negation and uncertainty
Negation and uncertainty modeling are long-standing tasks in natural language processing.
Linguistic theory postulates that expressions of negation and uncertainty are semantically …
Linguistic theory postulates that expressions of negation and uncertainty are semantically …