One of the classic techniques for image annotation is the language translation model. It views an image as a document, i.e., a set of visual words which are obtained by vector quatitizing the image regions generated by unsupervised image segmentation. ...

Dictionary-based approaches to query translation have been widely used in Cross-Language Information Retrieval (CLIR) experiments. However, translation has been not only limited by the coverage of the dictionary, but also affected by translation ...

Contextual Advertising is a type of Web advertising, which, given the URL of a Web page, aims to embed into the page (typically via JavaScript) the most relevant textual ads available. For static pages that are displayed repeatedly, the matching of ads ...

Many artificial intelligence tasks, such as automated question answering, reasoning or heterogeneous database integration, involve verification of a semantic category (e.g. "coffee" is a drink, "red" is a color, while "steak" is not a drink and "big" is ...

We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity ...

Conventional question answering (QA) techniques independently process candidate-bearing snippets to select an exact answer to a question from candidate answers. This paper presents two novel ways of utilizing redundancy in candidate-bearing snippets to ...

How to improve search accuracy for difficult topics is an under-addressed, yet important research question. In this paper, we consider a scenario when the search results are so poor that none of the top-ranked documents is relevant to a user's query, ...

In this paper, we present a novel technique of first performing document genre identification, then utilizing the genre for producing tailored summaries based on a user's information seeking needs - genre oriented goal-focused summarization - such as a ...

Opinion retrieval is a document retrieval process, which requires documents to be retrieved and ranked according to their opinions about a query topic. A relevant document must satisfy two criteria: relevant to the query topic, and contains opinions ...

Traditional adaptive filtering systems learn the user's interests in a rather simple way - words from relevant documents are favored in the query model, while words from irrelevant documents are down-weighted. This biases the query model towards ...

As one of the most effective query expansion approaches, local feedback is able to automatically discover new query terms and improve retrieval accuracy for different retrieval models. However, the performance of local feedback is heavily dependent on ...

Implicit feedback algorithms utilize interaction between searchers and search systems to learn more about users' needs and interests than expressed in query statements alone. This additional information can be used to formulate improved queries or ...

The term frequency normalisation parameter sensitivity is an important issue in the probabilistic model for Information Retrieval. A high parameter sensitivity indicates that a slight change of the parameter value may considerably affect the retrieval ...

This paper compares the effectiveness of two well-known query-dependent link-based ranking algorithms, "Hyperlink-Induced Topic Search" (HITS) and the "Stochastic Approach for Link-Structure Analysis" (SALSA). The two algorithms are evaluated on a very ...