Dávid Márk Nemeskey

Competence in lexical semantics

We investigate from the competence standpoint two recent models of lexical semantics, algebraic conceptual representations and continuous vector models.

Interest point and segmentation-based photo annotation

Our approach to the ImageCLEF 2009 tasks is based on image segmentation, SIFT keypoints and Okapi BM25-based text retrieval. We use feature vectors to describe the visual content of an image segment, a keypoint or the entire image. The features include color histograms, a shape descriptor as well as a 2D Fourier transform of a segment and an orientation histogram of detected keypoints. We trained a Gaussian Mixture Model (GMM) to cluster the feature vectors extracted from the image segments and keypoints independently. The normalized Fisher gradient vector computed from GMM of SIFT descriptors is a well known technique to represent an image with only one vector. Novel to our method is the combination of Fisher vectors for keypoints with those of the image segments to improve classification accuracy. We introduced correlation-based combining methods to further improve classification quality.

Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation

Multi-sense word embeddings (MSEs) model different meanings of word forms with different vectors. We propose two new methods for evaluating MSEs, one based on monolingual dictionaries, and the other exploiting the principle that words may be ambiguous as far as the postulated senses translate to different words in some other language.