  In this paper, we introduce the NameRec* task, which aims to do highly
accurate and fine-grained person name recognition. Traditional Named Entity
Recognition models have good performance in recognising well-formed person
names from text with consistent and complete syntax, such as news articles.
However, there are rapidly growing scenarios where sentences are of incomplete
syntax and names are in various forms such as user-generated contents and
academic homepages. To address person name recognition in this context, we
propose a fine-grained annotation scheme based on anthroponymy. To take full
advantage of the fine-grained annotations, we propose a Co-guided Neural
Network (CogNN) for person name recognition. CogNN fully explores the
intra-sentence context and rich training signals of name forms. To better
utilize the inter-sentence context and implicit relations, which are extremely
essential for recognizing person names in long documents, we further propose an
Inter-sentence BERT Model (IsBERT). IsBERT has an overlapped input processor,
and an inter-sentence encoder with bidirectional overlapped contextual
embedding learning and multi-hop inference mechanisms. To derive benefit from
different documents with a diverse abundance of context, we propose an advanced
Adaptive Inter-sentence BERT Model (Ada-IsBERT) to dynamically adjust the
inter-sentence overlapping ratio to different documents. We conduct extensive
experiments to demonstrate the superiority of the proposed methods on both
academic homepages and news articles.
