中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
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Updated
Jan 15, 2025 - Python
中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild
[EMNLP 2022] Unifying and multi-tasking structured knowledge grounding with language models
A relation-aware semantic parsing model from English to SQL
Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning, with applications in program synthesis and semantic parsing.
Multiple paper open-source codes of the Microsoft Research Asia DKI group
A KBQA system based on DBpedia.
ICLR 2022 Paper, SOTA Table Pre-training Model, TAPEX: Table Pre-training via Learning a Neural SQL Executor
[ACL 2024] Official resources of "ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models".
SoTA Abstract Meaning Representation (AMR) parsing with word-node alignments in Pytorch. Includes checkpoints and other tools such as statistical significance Smatch.
[ICML 2023] Data and code release for the paper "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation".
Provide Semantic Parsing solutions and Natural Language Inferences for multiple languages following the idea of the syntax-semantics interface.
A list of recent papers about Meta / few-shot learning methods applied in NLP areas.
Translating natural language questions to a structured query language
The Resources for "Natural Language to Logical Form" ; "自然语言转逻辑形式"研究资料收集。
Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
A dataset of complex questions on semi-structured Wikipedia tables
AMR Parsing as Sequence-to-Graph Transduction
[ACL 2021] This is the project containing source codes and pre-trained models about ACL2021 Long Paper ``LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations".
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