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Found 32 papers, 26 papers with code
Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking
Relation extraction (RE) is an important natural language processing task that predicts the relation between two given entities, where a good understanding of the contextual information is essential to achieve an outstanding model performance.
Ranked #11 on
Relation Extraction
on SemEval-2010 Task-8
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories
Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing
Dependency parsing is an important fundamental natural language processing task which analyzes the syntactic structure of an input sentence by illustrating the syntactic relations between words.
Ranked #2 on
Dependency Parsing
on Penn Treebank
Syntax-driven Approach for Semantic Role Labeling
To achieve a better performance in SRL, a model is always required to have a good understanding of the context information.
Ranked #2 on
Semantic Role Labeling
on CoNLL 2005
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards a given aspect term in a sentence on the fine-grained level, which usually requires a good understanding of contextual information, especially appropriately distinguishing of a given aspect and its contexts, to achieve good performance.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Enhancing Relation Extraction via Adversarial Multi-task Learning
Relation extraction (RE) is a sub-field of information extraction, which aims to extract the relation between two given named entities (NEs) in a sentence and thus requires a good understanding of contextual information, especially the entities and their surrounding texts.
ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition
Chinese word segmentation (CWS) and named entity recognition (NER) are two important tasks in Chinese natural language processing.
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts
Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs.
Relation Extraction with Word Graphs from N-grams
Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for in-domain scenarios.
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design
However, there are few specialized models in certain domains, such as interior design, which is attributed to the complex textual descriptions and detailed visual elements inherent in design, alongside the necessity for adaptable resolution.
A Systematic Review of Deep Learning-based Research on Radiology Report Generation
Specifically, we firstly cover pivotal RRG approaches based on the task-specific features of radiographs, reports, and the cross-modal relations between them, and then illustrate the benchmark datasets conventionally used for this task with evaluation metrics, subsequently analyze the performance of different approaches and finally offer our summary on the challenges and the trends in future directions.
Improving Image Captioning via Predicting Structured Concepts
Having the difficulty of solving the semantic gap between images and texts for the image captioning task, conventional studies in this area paid some attention to treating semantic concepts as a bridge between the two modalities and improved captioning performance accordingly.
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences
Recently, the increasing demand for superior medical services has highlighted the discrepancies in the medical infrastructure.
Ziya2: Data-centric Learning is All LLMs Need
Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.
Improving Arabic Diacritization with Regularized Decoding and Adversarial Training
Arabic diacritization is a fundamental task for Arabic language processing.
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks
Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities.
Ranked #12 on
Relation Extraction
on SemEval-2010 Task-8
Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble
It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words.
Ranked #2 on
Aspect-Based Sentiment Analysis (ABSA)
on MAMS
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Enhancing Aspect-level Sentiment Analysis with Word Dependencies
Aspect-level sentiment analysis (ASA) has received much attention in recent years.
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks
End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms.
Summarizing Medical Conversations via Identifying Important Utterances
For the particular dataset used in this study, we show that high-quality summaries can be generated by extracting two types of utterances, namely, problem statements and treatment recommendations.
Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams
However, their work on modeling such contextual features is limited to concatenating the features or their embeddings directly with the input embeddings without distinguishing whether the contextual features are important for the joint task in the specific context.
Improving Biomedical Named Entity Recognition with Syntactic Information
The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.
Ranked #1 on
Named Entity Recognition (NER)
on Species-800
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text.
Ranked #3 on
Named Entity Recognition (NER)
on WNUT 2016
Chinese Named Entity Recognition
named-entity-recognition
+2
Named Entity Recognition for Social Media Texts with Semantic Augmentation
In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.
Ranked #4 on
Named Entity Recognition (NER)
on WNUT 2016
Chinese Named Entity Recognition
named-entity-recognition
+3
Improving Constituency Parsing with Span Attention
Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task.
Ranked #1 on
Constituency Parsing
on ATB
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks
Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly.
Ranked #2 on
CCG Supertagging
on CCGbank
Improving Chinese Word Segmentation with Wordhood Memory Networks
Contextual features always play an important role in Chinese word segmentation (CWS).
Ranked #1 on
Chinese Word Segmentation
on CITYU
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge
Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks.
WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference
Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings.
ChiMed: A Chinese Medical Corpus for Question Answering
Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains.
