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
Nov 17, 2024 - 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.
A KBQA system based on DBpedia.
Multiple paper open-source codes of the Microsoft Research Asia DKI group
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.
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.
[ICML 2023] Data and code release for the paper "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation".
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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