Benjamin Sznajder

Beyond basic faceted search

This paper extends traditional faceted search to support richer information discovery tasks over more complex data models. Our first extension adds exible, dynamic business intelligence aggregations to the faceted application, enabling users to gain insight into their data that is far richer than just knowing the quantities of documents belonging to each facet. We see this capability as a step toward bringing OLAP capabilities, traditionally supported by databases over relational data, to the domain of free-text queries over metadata-rich content. Our second extension shows how one can efficiently extend a faceted search engine to support correlated facets - a more complex information model in which the values associated with a document across multiple facets are not independent. We show that by reducing the problem to a recently solved tree-indexing scenario, data with correlated facets can be efficiently indexed and retrieved

On the Retrieval of Wikipedia Articles Containing Claims on Controversial Topics

This work presents a novel claim-oriented document retrieval task. For a given controversial topic, relevant articles containing claims that support or contest the topic are retrieved from a Wikipedia corpus. For that, a two-step retrieval approach is proposed. At the first step, an initial pool of articles that are relevant to the topic are retrieved using state-of-the-art retrieval methods. At the second step, articles in the initial pool are re-ranked according to their potential to contain as many relevant claims as possible using several claim discovery features. Hence, the second step aims at maximizing the overall claim recall of the retrieval system. Using a recently published claims benchmark, the proposed retrieval approach is demonstrated to provide more relevant claims compared to several other retrieval alternatives.

A statistical approach to mining customers' conversational data from social media

In this paper, we present one possible way of analyzing social media conversional data in order to better understand customers. Ultimately, our goal is to analyze customer behavior as it is expressed in free-form conversations and extract from it commercially valuable information about the customer. In this study, we concentrate on using statistical techniques for analyzing this unstructured data at two levels: 1) at the level of the words used in the conversation and 2) by mapping those words to abstract concepts. The goal of such a statistical analysis is twofold. First, the statistically significant terms used by the users and the concepts associated with them provide insight on a users interests that commercial services can use, for example, in order to target advertisements. In addition, knowing the evolution of a customers interests and hobbies can be exploited commercially by retailers, media and entertainment companies, telecommunications companies, and more. In this paper, we describe a general framework for the analysis of social media data and, in turn, the application of the framework to the statistical analysis of the language of tweets.

A unified inverted index for an efficient image and text retrieval

We present an efficient method for approximate search in a combination of several metric spaces -- which are a generalization of low level image features -- using an inverted index. Our approximation gives very high recall with subsecond response time on a real data set of one million images extracted from Flickr. We further exploit the inverted index to improve efficiency of the query processing by combining our search in metric features with search in associated textual metadata.

Greaaaat bargains starting from just 99p!!!! :-): brand perception in the social media

In this work we discuss the challenges of utilizing social media data, and more specifically microblogs, for helping brand managers. Brand perception is one of the most important tasks of a brand manager, requiring to understand how customers perceive and select brands in specific product categories or market segments. While understanding the brand perception from conventional sources such as reviews and advertisement is well studied and established, gaining insights from social media sources is still an open challenge. In this paper, we present a high-level overview of a novel system that was developed in IBM which aims at extracting brand perception from Twitter. As a proof of concept, we present some preliminary results from the retail domain.