The USAS semantic tagger is a powerful language technology tool that has proven to be very effective in various applications such as content analysis, discourse analysis and information extraction. In the Benedict project, we intend to use the semantic taggers for the English and Finnish languages as search tools in electronic dictionaries, thereby enabling users to carry out context-sensitive ...

Synonyms extraction is a difficult task to achieve and evaluate. Some studies have tried to exploit general dictionaries for that purpose, seeing them as graphs where words are related by the definition they appear in, in a complex network of an arguably semantic nature. The advantage of using a general dictionary lies in the coverage, and the availability of such resources, in general and also...

The Bag-of-Visual-Words (BoVW) model is a popular approach for visual recognition. Used successfully in many different tasks, simplicity and good performance are the main reasons for its popularity. The central aspect of this model, the visual dictionary, is used to build mid-level representations based on low level image descriptors. Classifiers are then trained using these mid-level represent...

Periodic noise is a well-known problem in seismic exploration, caused by power lines, pump jacks, engine operation, or other interferences. It contaminates data and affects subsequent processing interpretation. The conventional methods to attenuate periodic are notch filtering some model-based methods. However, these either simultaneously events around the same frequencies, need expensive compu...

Bag-of-words document representations are often used in text, image and video processing. While it is relatively easy to determine a suitable word dictionary for text documents, there is no simple mapping from raw images or videos to dictionary terms. The classical approach builds a dictionary using vector quantization over a large set of useful visual descriptors extracted from a training set,...

Factorization of polyphonic musical signals remains a difficult problem due to the presence of overlapping harmonics. Existing dictionary learning methods cannot guarantee that the learned dictionary atoms are semantically meaningful. In this paper, we explore the factorization of harmonic musical signals when a fixed dictionary of harmonic sounds is already present. We propose a method called ...

Automatic Speech Recognition (ASR) systems that have even moderately large recognition vocabularies model these words as sequencesof subword units, or phonemes. The set of these phonemes, or the phoneset, forms the basic units that the ASR system is trained to classify. This set is usually small in size, consisting typically of about 40 phones for English. The ASR system uses a dictionary in wh...

The quality of a text-to-speech (TTS) system heavily depends on the transcription quality of the words to be spoken. Obviously the best transcription can be found in a phonetic dictionary. But for out of vocabulary (OOV) words fall back routines have to be developed. This paper proposes a fall back routine that combines the correctness of a phonetic dictionary with the exibility of a neural net...