Articles

We present a four-step hierarchical SRL strategy which generalizes the classical two-level approach (boundary detection and classification). To achieve this, we have split the classification step by group- ing together roles which share linguistic properties (e.g. Core Roles versus Ad- juncts). The results show that the non- optimized hierarchical approach …...

In this paper, we describe our systems for the CoNLL-2005 shared task. The aim of the task is semantic role labeling using a machine-learning algorithm. We apply the Support Vector Machines to the task. We added new features based on full parses and manually categorized words. We also report on …...

This paper describes a system for semantic role labeling for the CoNLL2005 Shared task. We divide the task into two sub-tasks: boundary recognition by a general tree- based predicate-argument recognition algo- rithm to convert a parse tree into a flat rep- resentation of all predicates and their related boundaries, and …...

We present an application of Sparse Bayesian Learning to the task of semantic role labeling, and we demonstrate that this method produces smaller classifiers than the popular Support Vector approach. We describe the classification strategy and the features used by the classifier. In par- ticular, the contribution of six parse …...

We present a semantic role labeling sys- tem submitted to the closed track of the CoNLL-2005 shared task. The system, in- troduced in (Toutanova et al., 2005), im- plements a joint model that captures de- pendencies among arguments of a predi- cate using log-linear models in a discrimi- native re-ranking …...

In this paper we apply conditional random fields (CRFs) to the semantic role labelling task. We define a random field over the structure of each sentence’s syntactic parse tree. For each node of the tree, the model must predict a semantic role label, which is interpreted as the labelling for …...

In this paper, we propose an approach for inferring semantic role using sub- categorization frames and maximum entropy model. Our approach aims to use the sub-categorization information of the verb to label the mandatory ar- guments of the verb in various possi- ble ways. The ambiguity between the assignment of …...

A maximum entropy classifier is used in our semantic role labeling system, which takes syntactic constituents as the labeling units. The maximum entropy classifier is trained to identify and classify the predi- cates’ semantic arguments together. Only the constituents with the largest probabil- ity among embedding ones are kept. Af- …...

We report on an active learning experi- ment for named entity recognition in the astronomy domain. Active learning has been shown to reduce the amount of la- belled data required to train a supervised learner by selectively sampling more in- formative data points for human annota- tion. We inspect double …...

We address the problem of learning a morphological automaton directly from a monolingual text corpus without re- course to additional resources. Like pre- vious work in this area, our approach ex- ploits orthographic regularities in a search for possible morphological segmentation points. Instead of affixes, however, we search for affix …...

We present a discrete optimization model based on a linear programming formulation as an alternative to the cascade of classifiers implemented in many language processing systems. Since NLP tasks are correlated with one another, sequential processing does not guarantee optimal solutions. We apply our model in an NLG application and …...

Analogical learning is based on a two- step inference process: (i) computation of a structural mapping between a new and a memorized situation; (ii) transfer of knowledge from the known to the un- known situation. This approach requires the ability to search for and exploit such mappings, hence the need …...

Unsupervised learning algorithms based on Expectation Maximization (EM) are often straightforward to implement and provably converge on a local likelihood maximum. However, these algorithms of- ten do not perform well in practice. Com- mon wisdom holds that they yield poor results because they are overly sensitive to initial parameter values …...

Natural language interfaces designed for situationally embedded domains (e.g. cars, videogames) must incorporate knowledge about the users’ context to address the many ambiguities of situated language use. We introduce a model of situated language acquisition that operates in two phases. First, intentional context is represented and inferred from user actions …...

We describe a data-driven approach to building interpretable discourse structures for appointment scheduling dialogues. We represent discourse structures as headed trees and model them with probabilis- tic head-driven parsing techniques. We show that dialogue-based features regard- ing turn-taking and domain specific goals have a large positive impact on perfor- mance. …...

The classification problem derived from information extraction (IE) has an imbal- anced training set. This is particularly true when learning from smaller datasets which often have a few positive training examples and many negative ones. This paper takes two popular IE algorithms – SVM and Perceptron – and demonstrates how …...

This paper proposes a model for term re- occurrence in a text collection based on the gaps between successive occurrences of a term. These gaps are modeled using a mixture of exponential distributions. Pa- rameter estimation is based on a Bayesian framework that allows us to fit a flexi- ble …...

We present a system for computing sim- ilarity between pairs of words. Our sys- tem is based on Pair Hidden Markov Mod- els, a variation on Hidden Markov Mod- els that has been used successfully for the alignment of biological sequences. The parameters of the model are automatically learned from …...

In this paper we propose and evaluate a technique to perform semi-supervised learning for Text Categorization. particular we defined a kernel function,In namely the Domain Kernel, that allowed us to plug “external knowledge” into the supervised learning process. External knowledge is acquired from unlabeled data in a totally unsupervised way, …...

Clustering is an optimization procedure that partitions a set of elements to optimize some criteria, based on a fixed distance metric de- fined between the elements. Clustering ap- proaches have been widely applied in natural language processing and it has been shown re- peatedly that their success depends on defin- …...