This paper presents the approaches used by the MIRACLE team to image retrieval at ImageCLEF 2005. Text-based and content-based techniques have been tested, along with combination of both types of methods to improve image retrieval. The text-based experiments defined this year try to use semantic information sources, like thesaurus with semantic data or text structure. On the other hand, content...

We present an automated Image Annotation system called I-Tag which uses both visual and textual information of the images and recommends relevant tags for them. The automatic generation of metadata would allow image searches and content-based image retrieval (CBIR) to be more effective. We use state of the art tools on text based retrieval and image content based retrieval to retrieve similar i...

This paper reports the methods used by the EHIME team for textual entailment recognition in NTCIR-10, RITE-2. We participated in the Japanese BC subtask and Japanese MC subtask. We used Markov logic to infer textual entailment relations. In our Markov logic network, words and hyponyms are used as features.

We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these ...

Content_based image retrieval is a promising approach because of its automatic indexing, recognition and retrieval. This paper is a contribution in the field of the content Based Image Retrieval (CBIR). Objects are represented by their outlines shapes (silhouettes) and described following the XLWDOS Textual Description (Larabi et al., 2003). Textual Descriptors are sensitive to noise. The autho...

Social media is increasingly becoming a significant fraction of the content retrieved daily by Web users. However, the potential lack of quality of user generated content poses a challenge to information retrieval services, which rely mostly on textual features generated by users (particularly tags) commonly associated with the multimedia objects. This paper presents what, to the best of our kn...

NATURAL LANGUAGE INFERENCE: FROM TEXTUAL ENTAILMENT TO CONVERSATION ENTAILMENT By Chen Zhang Automatic inference from natural language is a critical yet challenging problem for many language-related applications. To improve the ability of natural language inference for computer systems, recent years have seen an increasing research effort on textual entailment. Given a piece of text and a hypot...

The task of visual concept detection, annotation, and retrieval using Flickr photos at ImageCLEF 2012 was organized as two subtasks: concept annotation and concept retrieval. In this paper, we present the effort of KIDS lab for the two subtasks. The proposed approaches combine various visual and textual features, dimension reduction methods, the random forest classification models, and the semi...

In recent years, a number of large-scale writing assessments (e.g., TOEFL iBT) have employed integrated writing tests to measure test takers’ academic writing ability. Using a quantitative method, the current study examined how written textual features and use of source material(s) varied across two types of text-based integrated writing tasks (i.e., listening-to-write vs. reading-to-write) and...

This paper presents an approach to computer-assisted teaching of reading abilities using corpus data. The approach is supported by a set of tools for automatically selecting and classifying texts retrieved from the Internet. The approach is based on a linguistic model of textual cohesion which describes relations between larger textual units that go beyond the sentence level. We show that textu...