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This tutorial explains how to use Python's re module for regular expression operations, covering basic string matching, character classes, quantifiers, grouping, greedy vs. non‑greedy matching, substitution, and a practical example of extracting email addresses from text.
This talk presents a series of graph‑based techniques for natural language processing, including the Story Forest system for hot event discovery, the GIANT framework for ontology creation and user interest modeling, and a divide‑and‑conquer approach to long‑text matching that leverages graph neural networks and community detection.
WanFang upgraded its academic paper retrieval system by adopting PaddleNLP’s Chinese pre‑trained Sentence‑BERT models, using weakly supervised SimCSE data and Milvus vector indexing, compressing the transformer for TensorRT‑accelerated inference, achieving 70% better matching quality and 2600 QPS latency‑optimized performance.
This article details the authors' approach to the 2021 Sohu Text Matching competition, describing the task definition, data splits, model architectures (cross‑encoder and bi‑encoder), pretrained language models used, various training tricks, ensemble strategies, and the resulting evaluation scores.
This article presents a comprehensive technical summary of the 2021 Sohu Campus Text Matching Algorithm Competition, detailing data characteristics, preprocessing strategies, tokenization choices, positional encoding methods, model architectures using relative encodings such as WoBERT and RoFormer, experimental results, and reflections on future improvements.
This article describes the end‑to‑end design and implementation of a virtual category‑tag framework for 58 local services, detailing data preparation, tag selection via semantic similarity models, tag mounting, synonym normalization, experimental comparisons of CDSSM, MatchPyramid, BERT, RoBERTa and other techniques, and outlines future improvements.
This article evaluates classic Siamese networks, various BERT‑based pretrained models, and several training tricks such as adversarial training, k‑fold cross‑validation, and model ensembling on both a public similarity‑sentence competition dataset and an internal voice‑assistant standard question matching dataset, ultimately raising accuracy from 97.23 % to 99.5 %.
The article reviews the principles of popular pretrained language models, compares their architectures, and details how NetEase Yanxuan applied BERT, GPT and ELMo to classification, matching, sequence labeling and generation tasks, presenting experimental results and deployment insights.
This announcement describes the rise of AI‑driven intelligent customer service, highlights 58.com’s long‑standing system, and introduces a competition that provides real‑world data for participants to develop advanced text‑matching and classification models using state‑of‑the‑art NLP techniques.
The announcement introduces an AI‑driven intelligent customer service competition, highlighting the importance of text matching and classification in NLP, describing 58.com’s existing system, providing a real‑world dataset, and inviting participants to develop precise models using the latest deep‑learning techniques.
AlphaEmbedding builds a weighted graph linking Chinese words, sub‑words, characters and pinyin, then uses random‑walk‑based node2vec training to produce embeddings that capture orthographic and phonetic similarity, markedly improving recall and ranking for homophones, typos and OOV terms in enterprise search.