In this paper we report on our participation in the CLEF-IP 2011 prior art retrieval task. We investigated whether adding syntactic information in the form of dependency triples to a bag-of-words representation could lead to improvements in patent retrieval. In our experiments, we investigated this effect on the title, abstract and first 400 words of the description section. The experiments wer...

This research looks at the effects of word order and segmentation on translation retrieval performance for an experimental Japanese-English translation memory system. We implement a number of both bag-of-words and word order-sensitive similarity metrics, and test each over characterbased and word-based indexing. The translation retrieval performance of each system configuration is evaluated emp...

In this paper we describe our TRECVID 2009 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interactive search. Starting point for the MediaMill concept detection approach is our top-performing bag-of-words system of last year, which uses multiple color descriptors, codebooks with soft-assignment, and kernel-based supervised l...

Representing a document as a bag-of-words and using keywords to retrieve relevant documents have seen a great success in large scale information retrieval systems such as Web search engines. Bag-of-words representation is computationally efficient and with proper term weighting and document ranking methods can perform surprisingly well for a simple document representation method. However, such ...

Abstract Dual encoders perform retrieval by encoding documents and queries into dense low-dimensional vectors, scoring each document its inner product with the query. We investigate capacity of this architecture relative to sparse bag-of-words models attentional neural networks. Using both theoretical empirical analysis, we establish connections between dimension, margin gold lower-ranked docum...

The state-of-the-art algorithms for large visual content recognition and content based similarity search today use the “Bag of Features” (BoF) or “Bag of Words” (BoW) approach. The idea, borrowed from text retrieval, enables the use of inverted files. A very well known issue with the BoF approach is that the query images, as well as the stored data, are described with thousands of words. This p...

In this paper we propose a new approach for the genre tagging task of videos, using only their ASR transcripts and associated metadata. This new approach is based on calculating the semantic similarity between the nouns detected in the video transcripts and a bag of nouns generated from WordNet, for each category proposed to classify the videos. Specifically, we have used the Lin measure based ...

Considering the growing amount of digital images in all kind of databases, the search for specific content remains a problem. Content-based image retrieval based on local features is a promising approach but comes with the problem of being memory and computational intensive. The bag of keypoints approach reduces the feature vectors to one histogramm per image. This paper shows an efficient clus...

Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and ...

Practically all information retrieval based approaches developed to date for automatic bug localization are based on the bag-of-words assumption that ignores any positional and ordering relationships between the terms in a query. In this paper, we argue that bug reports are ill-served by this assumption because such reports frequently contain various types of structural information whose terms ...