The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task. Recently, the Transformer is broadly adopted in various Natural Language Processing (NLP) tasks owing to its parallelism and advantageous performance. Nevertheless, the performance of the Transformer in NER is not as good as it is in other NLP tasks. In this paper, we propose TENER, a NER architecture adopting adapted Transformer Encoder to model the character-level features and word-level features. By incorporating the direction and relative distance aware attention and the un-scaled attention, we prove the Transformer-like encoder is just as effective for NER as other NLP tasks.
End-to-End Spoken Language Understanding Without Full Transcripts
IBM（国际商业机器公司）
口语语言理解（SLU）的一个重要组成部分是槽填充：使用语义实体标签表示口语话语的含义。在本文中，我们开发了端到端（E2 E）口语语言理解系统，直接转换语音输入的语义实体和调查，如果这些E2 E SLU模型可以单独训练语义实体注释没有字对字的成绩单。训练这样的模型非常有用，因为它们可以大大降低数据汇集的成本。我们创建了两种类型的语音到实体模型，CTC模型和基于注意力的编码器-解码器模型，通过调整最初为语音识别训练的模型。鉴于我们的实验涉及语音输入，这些系统需要正确识别实体标签和表示实体值的单词。对于我们在ATIS语料库上进行的语音到实体的实验，CTC和注意力模型都显示出令人印象深刻的跳过非实体单词的能力：当只训练实体与完整的转录时，几乎没有退化。我们还探讨了实体的顺序不一定与话语中的口语顺序相关的情况。由于注意力模型具有重新排序的能力，因此它的表现非常好，在语音到实体袋的F1分数中只降低了约2%。
Harnessing Large Language Models for Biomedical Named Entity Recognition
Department of Data Science and Big Data Technology, Hainan University, Haikou 570228, China（数据科学与大数据技术学院，海南大学，海口570228，中国）；Department of Mathematics, Hainan University, Haikou 570228, China（数学学院，海南大学，海口570228，中国）；Department of Population Health Sciences, Weill Cornell Medicine, New York 10022, USA（流行病学与公共卫生科学学院，韦尔·柯尔医学中心，纽约10022，美国）
From Retrieval to Reasoning: A Framework for Cyber Threat Intelligence NER with Explicit and Adaptive Instructions
Sichuan University（四川大学）；Renmin University of China（中国人民大学）；Wuhan University（武汉大学）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education, China（教育部下一代智能搜索与推荐工程研究中心）
