This paper presents a two-stage approach to summarizing multiple contrastive viewpoints in opinionated text. In the first stage, we use an unsupervised probabilistic approach to model and extract multiple viewpoints in text. We experiment with a variety of lexical and syntactic features, yielding significant performance gains over bag-of-words feature sets. In the second stage, we introduce Com...

In this paper we consider an object categorization system using local HMAX features. Two feature matching techniques are compared: the MAX technique, originally proposed in the HMAX framework, and the histogram technique originating from Bag-of-Words literature. We have found that each of these techniques have their own field of operation. The histogram technique clearly outperforms the MAX tec...

This paper describes the participation of the Language and Reasoning Group of UAM at RepLab 2013 Profiling evaluation lab. We adopted Distributional Term Representations (DTR) for facing the following problems: i) filtering tweets that are related to an entity, and ii) identifying positive or negative implications for the entity’s reputation, i.e., polarity for reputation. Distributional Term R...

In this paper, we describe our methods for ImageCLEF 2013 Personal Photo Retrieval Task. We devote our attention to making our system efficient in retrieving documents which have the similar topic with few query data. We train a ranking function using rankSVM. We extract Fisher Vectors (FVs) from several local descriptors as visual features, and use Bag-of-Words (BoW) as metadata features. The ...

Word embeddings have become widelyused in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words. In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clusterin...

Systemic features use linguisticallyderived language models as a basis for text classification. The graph structure of these models allows for feature representations not available with traditional bag-of-words approaches. This paper explores the set of possible representations, and proposes feature selection methods that aim to produce the most compact and effective set of attributes for a giv...

This paper investigates the capabilities of the Bag-of-Words (BWs) method in the 3D shape retrieval field. The contributions of this paper are (1) the 3D shape retrieval task is categorized from different points of view: specific versus generic, partial-toglobal retrieval (PGR) versus global-to-global retrieval (GGR), and articulated versus nonarticulated (2) the spatial information, represente...

In the context of the CLEF-IP 2010 classification task, we conducted a series of experiments with the Linguistic Classification System (LCS). We compared two document representations for patent abstracts: a bag-of-words representation and a syntactic/semantic representation containing both words and dependency triples. We evaluated two types of output: using a fixed cut-off on the ranking of th...

We present the BossaNova scheme for the ImageCLEF 2012 Flickr Photo Annotation Task. BossaNova is a mid-level image representation, recently developed by our team, that enriches the Bag-of-Words representation, by keeping a histogram of distances between the descriptors found in the image and those in the codebook. Our scheme has the advantage of being conceptually simple, non-parametric, and e...

A set of words labelled with their prior emotion is an obvious place to start on the automatic discovery of the emotion of a sentence, but it is clear that context must also be considered. No simple function of the labels on the individual words may capture the overall emotion of the sentence; words are interrelated and they mutually influence their affectrelated interpretation. We present a me...