Query clustering is crucial for automatically discovering frequently asked queries (FAQs) or most popular topics on a question-answering search engine. Due to the short length of queries, the traditional approaches based on keywords are not suitable for ...

The Web is a valuable source of language specific resources but collecting, organizing and utilizing this information is difficult. We describe CorpusBuilder, an approach for automatically generating Web-search queries to collect documents in a minority ...

Topic segmentation is an important initial step in many text-based tasks. A hierarchical representation of a texts topics is useful in retrieval and allows judging relevancy at different levels of detail. This short paper describes research on generic ...

We present a system that offers a new way of assessing web document relevance and new approach to the web-based evaluation of such a system. Provisionally named WebDocSum, the system is a query-biased web page summariser that aims to provide an ...

algorithm for the segmentation of an audio/video source into topically cohesive segments based on automatic speech recognition (ASR) transcriptions is presented. A novel two-pass algorithm is described that combines a boundary-based method with a ...

Our English-Chinese cross-language IR system is trained from parallel corpora; we investigate its performance as a function of training corpus size for three different training corpora. We find that the performance of the system as trained on the three ...

This paper presents an approach to automatically extracting the bilingual translations of many Web query terms through mining the Web anchor texts. Some preliminary experiments are conducted on using 109,416 Web pages containing both Chinese and English ...

There are many tasks that require information finding. Some can be largely automated, and others greatly benefit from successful interaction between system and searcher. We are interested in the task of answering questions where some synthesis of ...

In this paper we present the features of a Question/Answering (Q/A) system that had unparalleled performance in the TREC-9 evaluations. We explain the accuracy of our system through the unique characteristics of its architecture: (1) usage of a wide-...

Our goal is to automatically answer brief factual questions of the form ``When was the Battle of Hastings?'' or ``Who wrote The Wind in the Willows?''. Since the answer to nearly any such question can now be found somewhere on the Web, the problem ...

Hierarchies have long been used for organization, summarization, and access to information. In this paper we define summarization in terms of a probabilistic language model and use the definition to explore a new technique for automatically ...

We present a novel probabilistic method for topic segmentation on unstructured text. One previous approach to this problem utilizes the hidden Markov model (HMM) method for probabilistically modeling sequence data [7]. The HMM treats a document as ...

Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied extensively in other application areas such as speech ...

We investigate a meta-model approach, called Meta-learning Using Document Feature characteristics (MUDOF), for the task of automatic textual document categorization. It employs a meta-learning phase using document feature characteristics. Document ...

Topic distillation is the analysis of hyperlink graph structure to identify mutually reinforcing authorities (popular pages) and hubs (comprehensive lists of links to authorities). Topic distillation is becoming common in Web search engines, but the ...

Automatic summarization of open domain spoken dialogues is a new research area. This paper introduces the task, the challenges involved, and presents an approach to obtain automatic extract summaries for multi-party dialogues of four different genres, ...

We describe a text categorization approach that is based on a combination of feature distributional clusters with a support vector machine (SVM) classifier. Our feature selection approach employs distributional clustering of words via the recently ...

Thresholding strategies in automated text categorization are an underexplored area of research. This paper presents an examination of the effect of thresholding strategies on the performance of a classifier under various conditions. Using k-Nearest ...