# Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
## 1 Introduction
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Sensitivity Class** & **Description** & **Label** & **Count** \\ \hline
**Physical Contact** & Information that allows an individual to be contacted or located & \\
**Information** & in the physical world—such as telephone number or address. & \\
**Online Contact** & Information that allows an individual to be contacted or located & \\
**Information** & on the Internet—such as email. Often, this information is & \\
**Information** & independent of the specific computer used to access the network. & \\  & (See the category “Computer Information”) & \\  & Non-financial identifiers, excluding government-issued & \\
**Unique Identifiers** & identifiers, issued for purposes of consistently identifying or & \\  & recognising the individual. These include identifiers issued by a & \\  & Web site or service. & \\
**Purchase Information** & Information actively generated by the purchase of a product or & \\  & service, including information about the method of payment. & \\  & Information about an individual?s finances including account & \\  & status and activity information such as account balance, payment & \\
**Financial Information** & or overdraft history, and information about an individual’s & \\  & purchase or use of financial instruments including credit or debit & \\  & card information. & \\  & Information about the computer system that the individual is & \\
**Computer Information** & using to access the network—such as the IP number, domain & \\  & name, browser type or operating system. & \\
**Demographic and** & Data about an individual’s characteristics—such as gender, age, & \\
**Socioeconomic Data** & income, postal code, or geographic region. & \\
**State Management** & Mechanisms for maintaining a stateful session with a user or & \\
**Mechanisms** & automatically recognising users who have visited a particular site & \\  & or accessed particular content previously—such as HTTP cookies. & \\  & Membership in or affiliation with groups such as religious & \\
**Political Information** & organisations, trade unions, professional associations, political & \\  & parties, etc. & \\  & Information about an individual’s physical or mental health, & \\
**Health Information** & sexual orientation, use or inquiry into health care services or & \\  & products, and purchase of health care services or products. & \\
**Preference Data** & Data about an individual’s likes and dislikes—such as favorite & \\  & colour or musical tastes. & \\  & Information that can be used to identify an individual’s current & \\
**Location Data** & physical location and track them as their location changes—such & \\  & as GPS position data. & \\
**Government-issued** & Identifiers issued by a government for purposes of consistently & \\
**Identifiers** & identifying the individual. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensitivity classes used for multi-class classification. (Source: P3P Standard [4]) and the count of sensitive tokens in our dataset per class. task. We develop our models for _semantic_ and _temporal_ contexts, as this provides an adequate proof-of-concept, and as incorporating all four contexts requires extraneous methodologies, which are planned for future work. We reduce the problem of sensitivity annotation to a multi-class classification problem and follow deep learning techniques that were proven effective in similar labelling tasks, such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging [5; 6; 7]. We develop a context-aware classifier based on the BiLSTM + CRF architecture with word embeddings and WordShape as features. We address dataset limitations by developing data generation algorithms to combine synthetic and real data, and experimentally identify the best performing model architecture and feature combination. The models are first evaluated on a dataset generated with the assistance of synthetic sensitive data, reaching an F1 score of 96.73%.