AbstractMulti-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks even datasets are often manually constructed consist of mostly clean samples where all modalities well-correlated with the content. Thus, current video-text literature largely focuses on video titles or audio transcripts, while ignoring user comments, since users te...

3D object retrieval is an important yet challenging task that has drawn more and attention in recent years. While existing approaches have made strides addressing this issue, they are often limited to restricted settings such as image sketch queries, which unfriendly interactions for common users. In order overcome these limitations, paper presents a novel SHREC challenge track focusing on text...

Many real-world applications increasingly involve both structured data and text. Hence, managing both in an efficient and integrated manner has received much attention from both the IR and database communities. To date, however, little research has been devoted to semantic issues in the integration of text and data. In this paper we introduced a problem in this realm: entity retrieval. Given da...

There have been four Text REtrieval Conferences (TRECs); TREC-1 in November 1992, TREC-2 in August 1993, TREC-3 in November 1994 and TREC-4 in November 1995. The number of participating systems has grown from 25 in TREC-1 to 36 in TREC-4, including most of the major text retrieval software companies and most of the universities doing research in text retrieval (see table for some of the partici...

This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that both retrieves documents from a full-text document corpus and from within individual documents, and locates passages likely to contain information about important problem-solving features of cases. SPIRE uses two case-bases, one containing past precedents, and one containing excerpt...

There have been four Text REtrieval Conferences (TRECs); TREC-1 in November 1992, TREC-2 in August 1993, TREC-3 in November 1994 and TREC-4 in November 1995. The number of participating systems has grown from 25 in TREC-1 to 36 in TREC-4, including most of the major text retrieval software companies and most of the universities doing research in text retrieval (see table for some of the partici...

To solve the problem of indexing collections with diverse text documents, image documents, or documents with both text and images, one needs to develop a model that supports heterogeneous types of documents. In this paper, we show how information theory supplies us with the tools necessary to develop a unique model for text, image, and text/image retrieval. In our approach, for each possible qu...

Structured text retrieval models provide a formal definition or mathematical framework for querying semistructured textual databases. A textual database contains both content and structure. The content is the text itself, and the structure divides the database into separate textual parts and relates those textual parts by some criterion. Often, textual databases can be represented as marked up ...

Document retrieval in languages with a rich and complex morphology – particularly in terms of derivation and (single-word) composition – suffers from serious performance degradation with the stemming-only query-term-totext-word matching paradigm. We propose an alternative approach in which morphologically complex word forms are segmented into relevant subwords (such as stems, prefixes, suffixes...

Text classification is a core technique for text mining and information retrieval. It has been applied to many applications in many different research and industrial areas. Term weighting schemes have to assign an appropriate weight to each term to obtain a high text classification performance. Although term weighting is one of the important modules for text classification, and text classificat...