Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for “recency” queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We present an extensive experimental evaluation using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examine several techniques for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. We show that our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.

Named Entities (NEs) play an important role in many natural language and speech processing tasks. A resource that identifies relations between NEs could potentially be very useful. We present such automatically generated knowledge resource from Wikipedia, Named Entity Network (NE-NET), that provides a list of related Named Entities (NEs) and the degree of relation for any given NE. Unlike some manually built knowledge resource, NE-NET has a wide coverage consisting of 1.5 million NEs represented as nodes of a graph with 6.5 million arcs relating them. NE-NET also provides the ranks of the related NEs using a simple ranking function that we propose. In this paper, we present NE-NET and our experiments showing how NE-NET can be used to improve the retrieval of spoken (Broadcast News) and text documents.

We are experiencing an unprecedented increase of content contributed by users in forums such as blogs, social networking sites and microblogging services. Such abundance of content complements content on web sites and traditional media forums such as news papers, news and financial streams, and so on. Given such plethora of information there is a pressing need to cross reference information across textual services. For example, commonly we read a news item and we wonder if there are any blogs reporting related content or vice versa.

Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for "recency" queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We extensively evaluated our techniques using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examined several alternatives for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. Our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.

Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Faceted interfaces represent a new powerful paradigm that proved to be a successful complement to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases. In particular, we observe, through a pilot study, that facet terms rarely appear in text documents, showing that we need external resources to identify useful facet terms. For this, we first identify important phrases in each document. Then, we expand each phrase with ";context"; phrases using external resources, such as WordNet and Wikipedia, causing facet terms to appear in the expanded database. Finally, we compare the term distributions in the original database and the expanded database to identify the terms that can be used to construct browsing facets. Our extensive user studies, using the Amazon Mechanical Turk service, show that our techniques produce facets with high precision and recall that are superior to existing approaches and help users locate interesting items faster.

We demonstrate a fully working system for multifaceted browsing over large collections of text-annotated data, such as annotated images, that are stored in relational databases. Typically, such databases can be browsed across multiple facets (by topic, genre, location, and so on) and previous user studies showed that multifaceted interfaces improve substantially the ability of users to identify items of interest in the database. We demonstrate a scalable system that automatically generates multifaceted browsing hierarchies on top of a relational database that stores the underlying text-annotated objects. Our system supports a wide range of ranking alternatives for selecting and displaying the best facets and the best portions of the generated hierarchies, to facilitate browsing. We combine our ranking schemes with Rapid Serial Visual Presentation (RSVP), an advanced visualization technique, which further enhances the browsing experience and demonstrate how to use prefetching techniques to overcome the latency issues that are inherent when browsing the contents of a relational database using multifaceted interfaces.

Charisma, the ability to lead by virtue of personality alone, is difficult to define but relatively easy to identify. However, cultural factors clearly affect perceptions of charisma. In this paper we compare results from parallel perception studies investigating charismatic speech in Palestinian Arabic and American English. We examine acoustic/prosodic and lexical correlates of charisma ratings to determine how the two cultures differ with respect to their views of charismatic speech.

Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Interfaces that use multifaceted hierarchies represent a new powerful browsing paradigm which has been proven to be a successful complement to keyword searching. Thus far, multifaceted hierarchies have been created manually or semi-automatically, making it difficult to deploy multifaceted interfaces over a large number of databases. We present automatic and scalable methods for creation of multifaceted interfaces. Our methods are integrated with traditional relational databases and can scale well for large databases. Furthermore, we present methods for selecting the best portions of the generated hierarchies when the screen space is not sufficient for displaying all the hierarchy at once. We apply our technique to a range of large data sets, including annotated images, television programming schedules, and web pages. The results are promising and suggest directions for future research.