5 accepted papers
Named Entity Recognition (NER) is a fundamental task in natural language processing. Syntax plays a significant role in helping to recognize the boundaries and types of entities. In comparison to English, Chinese NER, due to the absence of explicit delimiters, often faces challenges in determining e…
The Flat-LAttice Transformer (FLAT) has achieved notable success in Chinese named entity recognition (NER) by integrating lexical information into the widely-used Transformer encoder. FLAT enhances each sentence by constructing a flat lattice, a token sequence with characters and matched lexicon wor…
Disease is one of the fundamental entities in biomedical research. Recognizing such entities from biomedical text and then normalizing them to a standardized disease vocabulary offer a tremendous opportunity for many downstream applications. Previous studies have demonstrated that joint modeling of…
Dual-Encoders is a promising mechanism for answer retrieval in question answering (QA) systems. Currently most conventional Dual-Encoders learn the semantic representations of questions and answers merely through matching score. Researchers proposed to introduce the QA interaction features in scorin…
Mammographic mass detection is an integral part of a computer-aided diagnosis system. Annotating a large number of mammograms at pixel-level in order to train a mass detection model in a fully supervised fashion is costly and time-consuming. This paper presents a novel self-training framework for se…