# Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
## 2 Related Work  
The majority of the literature on sensitivity labelling is associated with Data Loss Prevention (DLP) systems [8; 9; 10; 11; 12], notably focusing on classifying sensitivity at the document level. Other research uses sensitivity classification for confidential information redaction on declasified documents [13; 14; 15],where classification is often performed at finer granularity that reaches the token level. More general applications include quantifying information leakage in Open Social Networks (OSNs) for privacy-preserving technologies [16; 17].  
Earlier work on text sensitivity annotation focused on heuristics. _[PERSON]_[18] introduced a template matching approach with boolean hashtables to capture Personally Identifiable Information (PII) in medical records with 99-100% accuracy; however, this approach is challenging when dealing with unstructured data and requires manual work to be expanded to other domains. [PERSON] et al. [11] used NER to pinpoint sensitive tokens in a corpus. Although their assumptions about the sensitive nature of Named Entities are reasonable, static NER is context-free and only captures a limited part of sensitive content. [PERSON] et al. [19] presented an information-theoretic approach by introducing the concept of information content (IC), defined later in Section 3.4. They annotated as sensitive any noun-phrase with an IC value higher than a threshold \(\beta\). Again, this is a context-free approach and using only IC as a sensitivity measure is problematic in some cases, as it is directly related to the size and content of the corpus used.  
A different approach for text sensitivity annotation uses statistical machine learning models. The work of [PERSON] et al. offered a DLP system, which can classify sensitive enterprise documents using a Support Vector Machine (SVM) classifier trained on a WikiLeaks-based corpus [10]. Later, [PERSON] et al. built a novel SVM sensitivity classifier by mixing concepts from both NLP and machine learning for government document declassification, using POS n-gram tags as a sensitivity load indicator [15]. [PERSON] et al. [9] proposed another DLP system with more fine-grained granularity. Their work effectively combined unsupervised and supervised methods to create a similarity-based classifier operating on a paragraph level and trained on an ad-hoc annotated WikiLeaks corpus. Building on their previous work, [PERSON] et al. further enhanced their SVM classifier by introducing pre-trained Word2 Vec and GLoVe word embeddings [20; 21] and found that word embeddings can significantly contribute to a more accurate model. Our work is the closest to their approach.  
Research using deep learning for textual sensitivity annotation is relatively sparse, with only a few authors moving to that direction. [PERSON] et al. built a context-aware DLP system, which follows a hierarchical structure to achieve fine-grained granularity [8], and used LSTM neural networks to achieve binary sensitivity classification at the token level. Despite the novelty of their hierarchical approach, we argue that the dataset size used for the experiments may fall short of the requirements for deep learning applications. [PERSON] et al. [17] used LSTM networks for identifying personal health experiences from tweets. Similarly, previous work underlines the high utility of word embeddings and LSTM/BiLSTM networks for textual data mining and classification through social media posts and other sources [22; 23; 24]. Although these are framed in other problem domains, their results highlight the advantages of deep learning methodologies against conventional machine learning models for similar tasks.