Papers with Bi-LSTM
IxaMed at PharmacoNER Challenge 2019 (D19-57)
Copied to clipboard
| Challenge: | The aim of this paper is to present our approach in the PharmacoNER 2019 task. |
| Approach: | They propose to use a Bi-LSTM with a CRF to identify named entities from clinical case studies written in Spanish. |
| Outcome: | The proposed approach achieves the best score (86.81 F-Score) combining pretrained word embeddings of Wikipedia and Electronic Health Records with contextual string embedds. |
Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text (D19-57)
Copied to clipboard
| Challenge: | Currently, the number of biomedical literature is growing at an exponential rate. |
| Approach: | They propose a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the PharmaCoNER corpus . the proposed model is based on two bidirectional long-term memory and conditional random field networks . |
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models (D19-57)
Copied to clipboard
| Challenge: | NER and concept indexing perform named entity recognition and concept identifiers (CUIs) in a knowledge base. |
| Approach: | They propose a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI) they use bi-LSTM to capture the semantic information of a sequence and classify them into entities or no entities . |
| Outcome: | The proposed approach performs named entity recognition (NER) and concept indexing (CI) which links them to concept unique identifiers (CUIs) in a knowledge base. |
Augmenting pre-trained language models with audio feature embedding for argumentation mining in political debates (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies on the integration of multimodality with text and audio in natural language processing tasks have focused on the use of image and text for emotion recognition, fake news detection and document image classification. |
| Approach: | They propose to integrate audio features with text in a task of argumentation mining using a previously reported dataset and an audio-enhanced version. |
| Outcome: | The proposed model outperforms text-based models on a dataset of 28,850 utterances on 'argumentation mining' with limited data. |
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks (2020.coling-main)
Copied to clipboard
| Challenge: | End-to-end aspect-based sentiment analysis uses two sub-tasks to extract aspect terms . experimental results demonstrate the effectiveness of our approach on all datasets . |
| Approach: | They propose to combine aspect extraction and sentiment analysis with encoding syntactic information to improve model's representation of input sentences. |
| Outcome: | The proposed approach achieves state-of-the-art on three benchmark datasets. |
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)
Copied to clipboard
Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li
| Challenge: | Document AI parsing semi-structured image form is a key information extraction task. |
| Approach: | They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework. |
| Outcome: | The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings. |
Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse (C18-1)
Copied to clipboard
| Challenge: | Experimental results show that nuclearity recognition is a challenging task in Chinese discourse parsing due to the need for more deep semantic information. |
| Approach: | They propose a text matching network that encodes discourse units and paragraphs by combining Bi-LSTM and CNN to capture global dependency information and local n-gram information. |
| Outcome: | The proposed model outperforms baselines on the Chinese Discourse TreeBank . the proposed model is based on a novel text matching network . |
A structure-enhanced graph convolutional network for sentiment analysis (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on sentiment analysis and aspect-based sentiment analysis does not exploit syntactic information from dependency parsing. |
| Approach: | They propose a weighted graph convolutional network which exploits syntactic information . they use BERT instead of Bi-LSTM to generate contextualized representations as inputs . |
| Outcome: | The proposed model can exploit rich syntactic information based on feature combination . it can improve on four ABSA tasks out of six and two SA tasks out . |
Cascaded Semantic and Positional Self-Attention Network for Document Classification (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to document classification combine semantic information with positional information (word orders) . document classification is one of the fundamental problems in natural language processing . |
| Approach: | They propose a new architecture to combine semantic and positional information using a semantic self-attention layer cascaded with Bi-LSTM. |
| Outcome: | The proposed model can exploit the interaction between semantics and word positions in a more interpretable and adaptive manner while preserving a compact model size and high convergence rate. |
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
Copied to clipboard
| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Modeling Temporality of Human Intentions by Domain Adaptation (D18-1)
Copied to clipboard
| Challenge: | Recent research shows that themes and words within a conversation change across time, whereas topics and the patient's attitude towards their willingness to change might shift. |
| Approach: | They propose a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). |
| Outcome: | The proposed method improves on a college alcoholism dataset using a bi-LSTM and topic model to learn language usage change across different time sessions. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
Copied to clipboard
| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
A Label-Aware Autoregressive Framework for Cross-Domain NER (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored. |
| Approach: | They propose a framework that borrows entity information from the source domain to enhance NER in the target domain. |
| Outcome: | The proposed model improves over the state-of-the-art model on several datasets. |
Understanding the Cooking Process with English Recipe Text (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to translate recipes into a flow graph have performance problems . authors propose a framework to construct a graph from recipe text . |
| Approach: | They propose a framework that can be used to translate recipes into a flow graph representation. |
| Outcome: | The proposed framework can predict the edge label and achieve the overall F1 score of 92.2 on the English recipe flow graph corpus. |
SiNER: A Large Dataset for Sindhi Named Entity Recognition (2020.lrec-1)
Copied to clipboard
| Challenge: | Named entity recognition is an essential lower-level task in natural language processing (NLP). |
| Approach: | They propose to develop a named entity recognition dataset for low-resourced Sindhi language with quality baselines. |
| Outcome: | The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. |
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)
Copied to clipboard
| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
| Approach: | They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks. |
| Outcome: | The proposed method is the state-of-the-art in aspect-based sentiment classification. |
Learning distributed sentence vectors with bi-directional 3D convolutions (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods that render words or characters into images separately, but instead use text's visual features as input, we use 3-dimensional convolutions to learn distributed sentence representation. |
| Approach: | They propose to use text's visual features as input to learn distributed sentence representation using 3-dimensional sentence tensors and multiple 3-dimensional convolutions with different lengths are applied to the sentence . |
| Outcome: | The proposed model performs well on several downstream natural language processing tasks. |
Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM (L18-1)
Copied to clipboard
| Challenge: | Part-of-speech (POS) tagging is one of the most important building blocks in many natural language processing (NLP) applications. |
| Approach: | They propose to use a POS tagger for Arabic Gulf dialect to improve POS tagging accuracy. |
| Outcome: | The proposed POS tagger improves POS tagging accuracy for the Arabic Gulf dialect from 75% accuracy to 91% accuracy using a bi-LSTM labeler. |