Introduction
Billions of short texts are produced every day, in the form of search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Unlike documents, short texts have some unique characteristics which make them difficult to handle. 
First, short texts, especially search queries, do not always observe the syntax of a written language. This means traditional NLP techniques, such as syntactic parsing, do not always apply to short texts.
Second, short texts contain limited context. An analysis based on Bing's search logs shows that more than 97% of queries contain 1 to 8 words, and over 63% of queries only contain 1 or 2 words.
Because of the above reasons, short texts give rise to a significant amount of ambiguity, which makes them extremely difficult to handle.On the other hand, many applications, including search engines, ads, automatic question answering, online advertising, recommendation systems, etc., rely on short text understanding. In all these applications, the necessary first step is to transform an input text into a machine-interpretable representation, namely to "understand" the short text. A growing number of approaches leverage external knowledge to address the issue of inadequate contextual information that accompanies the short texts. These approaches can be classified into two categories: Explicit Representation Model (ERM) and Implicit Representation Model (IRM). In this tutorial, we will present a comprehensive overview of short text understanding based on explicit semantics (knowledge graph representation, acquisition, and reasoning) and implicit semantics (embedding and deep learning). Specifically, we will go over various techniques in knowledge acquisition, representation, and inferencing has been proposed for text understanding, and we will describe massive structured and semi-structured data that have been made available in the recent decade that directly or indirectly encode human knowledge, turning the knowledge representation problems into a computational grand challenge with feasible solutions insight.
Part I. Introduction (20 min) We will introduce the challenge of short text understanding, and its various applications, in order to motivate and inspire the audience of this problem area. This section will also provide a quick overview for the rest of the tutorial. 
Part II. Explicit short text understanding (80 min) We will introduce current popular knowledge base systems which are used for building explicit models. Then we will introduce the explicit representation such as conceptualization for segmentation, labeling, syntax structure analysis, and applications. 
Part III. Implicit short text understanding (60 min) We will introduce the major approaches used for building word embedding, phrase embedding, and sentence embedding. Then we will introduce how deep neural networks are built on top of these embedding for short text related applications.
Part IV. Conclusion (10 min) We will summarize the tutorial.
Data Mining and Enterprise Intelligence,Microsoft Research :