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Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

arXiv 1603.04351 ↗First posted 14 Mar 2016cs.CLcited by 55

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Abstract (arXiv)

We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Mar 2016.

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Citing papers per year: 2021: 16; 2022: 14; 2023: 13; 2024: 6; 2025: 4; 2026: 2.

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  1. 2021KLUE: Korean Language Understanding Evaluationcited by 113
  2. 2023Modeling rapid language learning by distilling Bayesian priors into artificial neural networkscited by 40
  3. 2023Pushing the Limits of ChatGPT on NLP Taskscited by 27
  4. 2021Entity Relation Extraction as Dependency Parsing in Visually Rich Documentscited by 23
  5. 2022How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processingcited by 16
  6. 2021Dependency Parsing as MRC-based Span-Span Predictioncited by 13
  7. 2021To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLPcited by 12
  8. 2024Emergent Word Order Universals from Cognitively-Motivated Language Modelscited by 11
  9. 2023Lexi: Self-Supervised Learning of the UI Languagecited by 10
  10. 2021A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languagescited by 9
  11. 2021A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021cited by 9
  12. 2021Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networkscited by 8
38 more
  1. 2021A Graph-Based Neural Model for End-to-End Frame Semantic Parsingcited by 7
  2. 2021LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsingcited by 7
  3. 2022Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecturecited by 7
  4. 2023Compositional Generalization without Trees using Multiset Tagging and Latent Permutationscited by 5
  5. 2024Exploiting Emotion-Semantic Correlations for Empathetic Response Generationcited by 5
  6. 2024A Truly Joint Neural Architecture for Segmentation and Parsingcited by 4
  7. 2022Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsingcited by 3
  8. 2022AUTOLEX: An Automatic Framework for Linguistic Explorationcited by 3
  9. 2022Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learningcited by 3
  10. 2022Data-driven Parsing Evaluation for Child-Parent Interactionscited by 3
  11. 2023Hexatagging: Projective Dependency Parsing as Taggingcited by 3
  12. 2024MRL Parsing Without Tears: The Case of Hebrewcited by 3
  13. 2021Syntactic Nuclei in Dependency Parsing -- A Multilingual Explorationcited by 2
  14. 2021DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AIcited by 2
  15. 2021Graph-Based Decoding for Task Oriented Semantic Parsingcited by 2
  16. 2022Compositional Generalization Requires Compositional Parserscited by 2
  17. 2022Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategiescited by 2
  18. 2022SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representationcited by 2
  19. 2023Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspectivecited by 2
  20. 2023Assessment of Pre-Trained Models Across Languages and Grammarscited by 2
  21. 2023On the Challenges of Fully Incremental Neural Dependency Parsingcited by 2
  22. 2021The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Taskcited by 1
  23. 2021Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognitioncited by 1
  24. 2021Cross-Register Projection for Headline Part of Speech Taggingcited by 1
  25. 2023Two Kinds of Recallcited by 1
  26. 2025Mapping the Course for Prompt-based Structured Predictioncited by 1
  27. 2021Transition-based Bubble Parsing: Improvements on Coordination Structure Predictioncited by 0
  28. 2022Biaffine Discourse Dependency Parsingcited by 0
  29. 2022Automatic Correction of Syntactic Dependency Annotation Differencescited by 0
  30. 2022Modeling Structure with Undirected Neural Networkscited by 0
  31. 2022LySACoru~na at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsingcited by 0
  32. 2022LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsingcited by 0
  33. 2023Another Dead End for Morphological Tags? Perturbed Inputs and Parsingcited by 0
  34. 2023Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structurescited by 0
  35. 2023Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programscited by 0
  36. 2023Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learningcited by 0
  37. 2024From LIMA to DeepLIMA: following a new path of interoperabilitycited by 0
  38. 2024Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLPcited by 0

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