Named Entity Recognition without Labelled Data: A Weak Supervision Approach

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1518–1533 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 1518 Named Entity Recognition without Labelled Data: A Weak Supervision Approach Pierre Lison1, Jeremy Barnes2, Aliaksandr Hubin1, and Samia Touileb2 1Norwegian Computing Center, Oslo, Norway 2 Language Technology Group, University of Oslo, Norway {plison,ahu}@nr.no, {jeremycb,samiat}@ifi.uio.no Abstract Named Entity Recognition (NER) perfor- mance often degrades rapidly when applied to target domains that differ from the texts ob- served during training. When in-domain la- belled data is available, transfer learning tech- niques can be used to adapt existing NER mod- els to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak su- pervision. The approach relies on a broad spec- trum of labelling functions to automatically an- notate texts from the target domain. These an- notations are then merged together using a hid- den Markov model which captures the vary- ing accuracies and confusions of the labelling functions. A sequence labelling model can fi- nally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news arti- cles from Reuters and Bloomberg) and demon- strate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model. 1 Introduction Named Entity Recognition (NER) constitutes a core component in many NLP pipelines and is employed in a broad range of applications such as information extraction (Raiman and Raiman, 2018), question answering (Moll´a et al., 2006), document de-identification (Stubbs et al., 2015), machine translation (Ugawa et al., 2018) and even conversational models (Ghazvininejad et al., 2018). Given a document, the goal of NER is to identify and classify spans referring to an entity belonging to pre-specified categories such as persons, organi- sations or geographical locations. NER models often rely on convolutional or re- current neural architectures, sometimes completed by a CRF layer (Chiu and Nichols, 2016; Lample et al., 2016; Yadav and Bethard, 2018). More re- cently, deep contextualised representations relying on bidirectional LSTMS (Peters et al., 2018), trans- formers (Devlin et al., 2019; Yan et al., 2019) or contextual string embeddings (Akbik et al., 2019) have also been shown to achieve state-of-the-art performance on NER tasks. These neural architectures require large corpora annotated with named entities, such as Ontonotes (Weischedel et al., 2011) or ConLL 2003 (Tjong Kim Sang and De Meulder, 2003). When only mod- est amounts of training data are available, transfer learning approaches can transfer the knowledge ac- quired from related tasks into the target domain, us- ing techniques such as simple transfer (Rodriguez et al., 2018), discriminative fine-tuning (Howard and Ruder, 2018), adversarial transfer (Zhou et al., 2019) or layer-wise domain adaptation approaches (Yang et al., 2017; Lin and Lu, 2018). However, in many practical settings, we wish to apply …

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