This paper addresses the task of document retrieval based on the degree of document
relatedness to the meanings of a query by presenting a semantic-enabled language model …

Recently, the focus of many novel search applications shifted from short keyword queries to
verbose natural language queries. Examples include question answering systems and …

Understanding short texts is crucial to many applications, but challenges abound. First, short
texts do not always observe the syntax of a written language. As a result, traditional natural …

Understanding search queries is a hard problem as it involves dealing with" word salad" text
ubiquitously issued by users. However, if a query resembles a well-formed question, a …

As more users across the world are interacting with dialog agents in their daily life, there is a
need for better speech understanding that calls for renewed attention to the dynamics …

We address the problem of query segmentation: given a keyword query, the task is to group
the keywords into phrases, if possible. Previous approaches to the problem achieve …

This paper first reveals the relationship between Inverse Document Frequency (IDF), a
global term weighting scheme, and information distance, a universal metric defined by …

Mining frequent patterns from a hidden dataset is an important task with 43 various real-life
applications. In this research, we propose a solution to this problem that is based on Markov …

Query segmentation is the problem of identifying those keywords in a query, which together
form compound concepts or phrases like" new york times". Such segments can help a …

In real life, many important datasets are not publicly accessible due to various reasons,
including privacy protection and maintenance of business competitiveness. However …