Arjun Akula (Arjun Reddy Akula)

This paper presents a novel approach to categorize, model and identify contextual information in natural language interface to database (NLIDB) systems. The interactions between user and system are categorized and modeled based on the way in which the contextual information is utilized in the interactions. A relationship schema among the responses (user and system responses) is proposed. We present a novel method to identify contextual information in one specific type of usersystem interaction. We report on results of experiments with the university related queries.

This paper presents a novel approach to building natural language interface to databases (NLIDB) based on Computational Paninian Grammar (CPG). It uses two distinct stages of processing, namely, syntactic processing followed by semantic processing. Syntactic processing makes the processing more general and robust. CPG is a dependency framework in which the analysis is in terms of syntacticosemantic relations. The closeness of these relations makes semantic processing easier and more accurate. It also makes the systems more portable.

Attribute information in a natural language query is one of the key features for converting a natural language query into a Structured Query Language (SQL) in Natural Language Interface to Database systems. In this paper, we explore the task of classifying the attributes present in a natural language query into different SQL clauses in a SQL query. In particular, we investigate the effectiveness of various features and Conditional Random Fields for this task. Our system uses a statistical classifier trained on manually prepared data. We report our results on three different domains and also show how our system can be used for generating a complete SQL query.

Service interactions account for major source of revenue and employment in many modern economies, and yet the service operations management process remains extremely complex. Ticket is the fundamental management entity in this process and resolution of tickets remains largely human intensive. A large portion of these human executed resolution tasks are repetitive in nature and can be automated. Ticket description analytics can be used to automatically identify the true category of the problem. This when combined with automated remediation actions considerably reduces the human effort. We look at monitoring data in a big provider's domain and abstract out the repeatable tasks from the noisy and unstructured human-readable text in tickets. We present a novel approach for automatic problem determination from this noisy and unstructured text. The approach uses two distinct levels of analysis, (a) correlating different data sources to obtain a richer text followed by (b) context based classification of the correlated data. We report on accuracy and efficiency of our approach using real customer data.

Many large IT service providers, having realized the transformational impact of cognitive technology, are experimenting with cognitive service agents like Amelia and IBM Watson for IT problem resolution. We build a system that extracts knowledge about different classes of problems arising in the IT infrastructure, mine problem linkages to recent system changes, and identify the resolution activities to mitigate problems. The system, at its core, uses data mining, machine learning, and natural language parsing techniques. By using extracted knowledge, one can (i) understand the kind of problems and the root causes affecting the IT infrastructure, (ii) proactively remediate the causes so that they no longer result in problems, and (iii) estimate the scope for automation for service management