In this paper we introduce a semantic role labeling system constructed on top of the full syntactic analysis of text. The labeling problem is modeled using a rich set of lexical, syntactic, and semantic attributes and learned using one-versus-all AdaBoost classifiers. Our results indicate that even a simple approach that assumes that each semantic argument maps into exactly one syntactic phrase...

In this paper, we present a unified knowledge based approach for sense disambiguation and semantic role labeling. Our approach performs both tasks through a single algorithm that matches candidate semantic interpretations to background knowledge to select the best matching candidate. We evaluate our approach on a corpus of sentences collected from various domains and show how our approach perfo...

This paper combines predictive labeling with dependency pairs and reports on its implementation. Our starting point is the method of proving termination of rewrite systems using semantic labeling with infinite models in combination with lexicographic path orders. We replace semantic labeling with predictive labeling to weaken the quasi-model constraints and we combine it with dependency pairs (...

In a goal-oriented spoken dialog system, the major aim of language understanding is to classify utterances into one or more of the pre-defined intents and extract the associated named entities. Typically, the intents are designed by a human expert according to the application domain. Furthermore, these systems are trained using large amounts of data manually labeled using an already prepared la...

We present a novel method for FrameNetbased semantic role labeling (SRL), focusing on limitations posed by the limited coverage of available annotated data. Our SRL model is based on Bayesian clustering and has the advantage of being very robust in the face of unseen and incomplete data. Frame labeling and role labeling are modeled in like fashions, allowing cascading classification scenarios. ...

This paper presents a semantic labeling technique based on information encoded in FrameNet. Sentences labeled for frames relevant to any new Information Extraction domain enable the automatic acquisition of extraction rules for the new domain. The experimental results show that both the semantic labeling and the extraction rules enabled by the labels are generated automatically with a high prec...

One major very highly repeated (VHR) DNA (approximately 7 X 10(6) copies/genome; repeat unit = 156 base pairs (bp)), a family of three minor VHR DNAs (approximately 2.8 X 10(6) copies/genome; repeat units = 71-74 bp), and a number of trace components account for almost 30% of the genome of a hermit crab. The repeat units of the three minor variants are defined by identical 14-bp G + C-rich inve...

In this paper, we present the details of the Arabic Semantic Labeling task. We describe some of the features of Arabic that are relevant for the task. The task comprises two subtasks: Arabic word sense disambiguation and Arabic semantic role labeling. The task focuses on modern standard Arabic.

This paper describes the HKPolyU-HKUST systems which were entered into the Semantic Role Labeling task in Senseval-3. Results show that these systems, which are based upon common machine learning algorithms, all manage to achieve good performances on the non-restricted Semantic Role Labeling task.

Pseudo-semantic labeling represents a novel approach for automatic content description of video. This information can be used in the context of a video database to improve browsing and searching. In this paper we will describe our work on using face detection techniques for pseudo-semantic labeling. We will present our results using a database of MPEG sequences.