Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2409–2419 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 2409 Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning Minlong Peng∗, Xiaoyu Xing∗, Qi Zhang, Jinlan Fu, Xuanjing Huang School of Computer Science, Fudan University, Shanghai, China {mlpeng16,xyxing18,qz,fujl16,xjhuang}@fudan.edu.cn Abstract In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionar- ies. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of the words constituting an entity. This greatly reduces the requirement on the quality of the dictionaries and makes our method generalize well with quite simple dictionaries. Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. We have published the source code at https:// github.com/v-mipeng/LexiconNER. 1 Introduction Named Entity Recognition (NER) is concerned with identifying named entities, such as person, location, product and organization names in un- structured text. It is a fundamental component in many natural language processing tasks such as machine translation (Babych and Hartley, 2003), knowledge base construction (Riedel et al., 2013; Shen et al., 2012), automatic question answering (Bordes et al., 2015), search (Zhu et al., 2005), etc. In this field, supervised methods, ranging from the typical graph models (Zhou and Su, 2002; McCallum et al., 2000; McCallum and Li, 2003; Settles, 2004) to current popular neural-network- based models (Chiu and Nichols, 2016; Lample et al., 2016; Gridach, 2017; Liu et al., 2018; Zhang ∗Equal contribution. Dictionary Simons David Anna Joe Bobick was managed by weight legend Joe Frazier Figure 1: Data labeling example for person names using our constructed dictionary. and Yang, 2018), have achieved great success. However, these supervised methods often require large scale fine-grained annotations (label every word of a sentence) to generalize well. This makes it hard to apply them to label-few domains, e.g., bio/medical domains (Del˙eger et al., 2016). In this work, we explore the way to perform NER using only unlabeled data and named entity dictionaries, which are relatively easier to obtain compared with labeled data. A natural practice to perform the task is to scan through the query text using the dictionary and treat terms matched with a list of entries of the dictionary as the entities (Nadeau et al., 2006; Gerner et al., 2010; Liu et al., 2015; Yang et al., 2018). However, this practice requires very high quality named entity dictionaries that cover most of entities, otherwise it will fail with poor performance. As shown in Figure 1, the constructed dictionary of person names only …

Content truncated to 500 words