Dinesh Raghu

Retrieving similar discussion forum threads: a structure based approach

Online forums are becoming a popular way of finding useful information on the web. Search over forums for existing discussion threads so far is limited to keyword-based search due to the minimal effort required on part of the users. However, it is often not possible to capture all the relevant context in a complex query using a small number of keywords. Example-based search that retrieves similar discussion threads given one exemplary thread is an alternate approach that can help the user provide richer context and vastly improve forum search results. In this paper, we address the problem of finding similar threads to a given thread. Towards this, we propose a novel methodology to estimate similarity between discussion threads. Our method exploits the thread structure to decompose threads in to set of weighted overlapping components. It then estimates pairwise thread similarities by quantifying how well the information in the threads are mutually contained within each other using lexical similarities between their underlying components. We compare our proposed methods on real datasets against state-of-the-art thread retrieval mechanisms wherein we illustrate that our techniques outperform others by large margins on popular retrieval evaluation measures such as NDCG, MAP, Precision@k and MRR. In particular, consistent improvements of up to 10% are observed on all evaluation measures.

A statistical approach for Non-Sentential Utterance Resolution for Interactive QA System

Non-Sentential Utterances (NSUs) are short utterances that do not have the form of a full sentence but nevertheless convey a complete sentential meaning in the context of a conversation. NSUs are frequently used to ask follow up questions during interactions with question answer (QA) systems resulting into in-correct answers being presented to their users. Most of the current methods for resolving such NSUs have adopted rule or grammar based approach and have limited applicability. In this paper, we present a data driven statistical method for resolving such NSUs. Our method is based on the observation that humans identify keyword appearing in an NSU and place them in the context of conversation to construct a meaningful sentence. We adapt the keyword to question (K2Q) framework to generate natural language questions using keywords appearing in an NSU and its context. The resulting questions are ranked using different scoring methods in a statistical framework. Our evaluation on a data-set collected using mTurk shows that the proposed method perform significantly better than the previous work that has largely been rule based.

Domain adaptive answer extraction for discussion boards

Answer extraction from discussion boards is an extensively studied problem. Most of the existing work is focused on supervised methods for extracting answers using similarity features and forum-specific features. Although this works well for the domain or forum data that it has been trained on, it is difficult to use the same models for a domain where the vocabulary is different and some forum specific features may not be available. In this poster, we report initial results of a domain adaptive answer extractor that performs the extraction in two steps: a) an answer recognizer identifies the sentences in a post which are likely to be answers, and b) a domain relevance module determines the domain significance of the identified answer. We use domain independent methodology that can be easily adapted to any given domain with minimum effort.

A Case Based Approach to Serve Information Needs in Knowledge Intensive Processes

Case workers who are involved in knowledge intensive business processes have critical information needs.When dealing with a case, they often need to check how similar cases were handled and what best practices, methods and tools proved useful. In this paper, we present our Solution Information Management SIM system developed to assist case workers by retrieving and offering targeted and contextual content recommendations to them. In particular, we present a novel method for intelligently weighing different fields in a case when they are used as context to derive recommendations. Experimental results indicate that our approach can yield recommendations that are approximately 15 more precise than those obtained through a baseline approach where the fields in the context have equal weights. SIM is being actively used by case workers in a large IT services company.