People working in an office environment suffer from large volumes of information that they need to manage and access. Frequently, the problem is due to machines not being able to recognise the many implicit relationships between office artefacts, and also due to them not being aware of the context surrounding them. In order to expose these relationships and enrich artefact context, text analytics can be employed over semi-structured and unstructured content, including free text. In this paper, we explain how this strategy is applied and partly evaluated for a specific use-case: supporting the attendees of a calendar event to prepare for the meeting.

Email can be considered as a virtual working environment in which users are constantly struggling to manage the vast amount of exchanged data. Although most of this data belongs to well-defined workflows, these are implicit and largely unsupported by existing email clients. Semanta provides this support by enabling Semantic Email ― email enhanced with machine-processable metadata about specific types of email Action Items (e.g. Task Assignment, Meeting Proposal). In the larger picture, these items form part of ad-hoc workflows (e.g. Task Delegation, Meeting Scheduling). Semanta is faced with a knowledge-acquisition bottleneck, as users cannot be expected to annotate each action item, and their automatic recognition proves difficult. This paper focuses on applying computationally treatable aspects of speech act theory for the classification of email action items. A rule-based classification model is employed, based on the presence or form of a number of linguistic features. The technology’s evaluation suggests that whereas full automation is not feasible, the results are good enough to be presented as suggestions for the user to review. In addition the rule-based system will bootstrap a machine learning system that is currently in development, to generate the initial training sets which are then improved through the user’s reviewing.

The lack of structure in the content of email messages makes it very hard for data channelled between the sender and the recipient to be correctly interpreted and acted upon. As a result, the purposes of messages frequently end up not being fulfilled, prompting prolonged communication and stalling the disconnected workflow that is characteristic of email. This problem could be partially solved by extending the current email model to support light-weight semantics pertaining to the intents of the sender and the expectations from the recipient(s), thus leaving no room for ambiguity. Semantically-aware email clients will then be able to support the user with the workflow of email-generated tasks. In line with this thinking, we present the sMail Conceptual Framework. At its core, this framework has an Email Speech Act Model. Given this model, email content can be categorized into a set of speech acts, each carrying specific expectations. In this paper we present and discuss the methodology and results of this model?s statistical evaluation. By performing the same evaluation on another existing model, we demonstrate our model?s higher sophistication. After careful observations, we perform changes to the model and subsequently accommodate the changes in the revised sMail Conceptual Framework.