Complex Question Answering: Unsupervised Learning Approaches and Experiments

Abstract

Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a set of documents with a minimum loss of relevant information. In this paper, we experiment with one empirical method and two unsupervised statistical machine learning techniques: K-means and Expectation Maximization (EM), for computing relative importance of the sentences. We compare the results of these approaches. Our experiments show that the empirical approach outperforms the other two techniques and EM performs better than K-means. However, the performance of these approaches depends entirely on the feature set used and the weighting of these features. In order to measure the importance and relevance to the user query we extract different kinds of features (i.e. lexical, lexical semantic, cosine similarity, basic element, tree kernel based syntactic and shallow-semantic) for each of the document sentences. We use a local search technique to learn the weights of the features. To the best of our knowledge, no study has used tree kernel functions to encode syntactic/semantic information for more complex tasks such as computing the relatedness between the query sentences and the document sentences in order to generate query-focused summaries (or answers to complex questions). For each of our methods of generating summaries (i.e. empirical, K-means and EM) we show the effects of syntactic and shallow-semantic features over the bag-of-words (BOW) features.

Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning

“…The multi-document summarization problem has received much attention recently (Lyngbaek, 2013;Sood, 2013;Qian and Liu, 2013) due to its ability to reduce large quantities of text to a human processable amount as well as its application in other fields such as question answering (Liu et al, 2008;Chali et al, 2009a;Chali et al, 2009b;Chali et al, 2011b). We expect this trend to further increase as the amount of linguistic data on the web from sources such as social media, wikipedia, and online newswire increases.…”

A Survey on Medical Text Mining

“…Many websites that are allowing question answering technology, allow end users to "ask" question to the computer and get exact or related answer [13]. Question and Answering technique utilizes multiple text mining methods.…”

Towards modeling question popularity in community question answering

“…The basic idea behind this method is that the higher the frequency a term occurs in one category than in the other, the more important this term is to this category. The rf weight of a term w j in Ω p can be calculated as: (6) where j w tp is the number of questions that contain w j in Ω p , j w fn denotes the number of questions in which w j appears in Ω u , and ε is a nonnegative constant which is set to 0 in this paper. The rf weight of w j in Ω u can also be calculated similarly.…”