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Classifying nouns into semantic categories (e.g., animals, food) is an important line of research in both cognitive science and natural language processing. We present a minimally supervised model for noun classification, which uses symmetric patterns (e.g., “X and Y”) and an iterative variant of the k-Nearest Neighbors algorithm. Unlike most previous works, we do not use a predefined set of sy...

We present an unsupervised topic model for short texts that performs soft clustering over distributed representations of words. We model the low-dimensional semantic vector space represented by the dense distributed representations of words using Gaussian mixture models (GMMs) whose components capture the notion of latent topics. While conventional topic modeling schemes such as probabilistic l...

This paper briefly describes our approach to the video genre tagging task which was a part of MediaEval 2011. We focused mainly on visual and audio information, and we exploited metadata and automatic speech transcripts only in a very basic way. Our approach relied on classification and on classifier fusion to combine different sources of information. We did not use any additional training data...

We are interested in the problem of extracting meaning structures from spoken utterances in human communication. In Spoken Language Understanding (SLU) systems, parsing of meaning structures is carried over the word hypotheses generated by the Automatic Speech Recognizer (ASR). This approach suffers from high word error rates and ad-hoc conceptual representations. In contrast, in this paper we ...

A major challenge in Spoken Dialogue Systems (SDS) is the detection of problematic communication (hotspots), as well as the classification of these hotspots into different types (root cause analysis). In this work, we focus on two classes of root cause, namely, erroneous speech recognition vs. other (e.g., dialogue strategy). Specifically, we propose an automatic algorithm for detecting hotspot...

We introduce a method for learning to assign suitable sentiment ratings to review articles. In our approach, reviews are transformed into collections of n-gram and semantic word class features aimed at maximizing the probability of classifying them into accurate ratings. The method involves automatically segmenting review articles into sentences and automatically estimating associations between...

Interdisciplinary research and development projects in medical engineering bene t from well selected collaboration partners. The process of nding such partners from often unfamiliar elds is di cult, but can be supported by an expert pro le that is based on patent analysis and classifying the patents to competence elds in medical engineering. Patent analysis and categorization are di cult and re...

This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) which identifies the relations between predicates and arguments, and relation classification (RC) which focuses on the relation between two entities or nominals. While mostly...