We present a general framework for semantic role labeling. The framework combines a machine learning technique with an integer linear programming based inference procedure, which incorporates linguistic and structural constraints into a global decision process. Within this framework, we study the role of syntactic parsing information in semantic role labeling. We show that full syntactic parsing information is, by far, most relevant in identifying the argument, especially, in the very first stage—the pruning stage. Surprisingly, the quality of the pruning stage cannot be solely determined based on its recall and precision. Instead, it depends on the characteristics of the output candidates that determine the difficulty of the downstream problems. Motivated by this observation, we propose an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves its performance. Our system has been evaluated in the CoNLL-2005 shared task on semantic role labeling, and achieves the highest F1 score among 19 participants.

This paper presents a Named Entity Extraction (NEE) system for the CoNLL 2002 competition. The two main sub-tasks of the problem, recognition (NER) and classification (NEC), are performed sequentially and independently with separate modules. Both modules are machine learning based systems, which make use of binary AdaBoost classifiers

We present a phrase recognition system based on perceptrons, and an online learning algorithm to train them together. The recognition strategy applies learning in two layers, first at word level, to filter words and form phrase candidates, second at phrase level, to rank phrases and select the optimal ones. We provide a global feedback rule which reflects the dependencies among perceptrons and allows to train them together online. Experimentation on Partial Parsing problems and Named Entity Extraction gives state-of-the-art results on the CoNLL public datasets. We also

Abstract. This work introduces a phrase recognition system based on perceptrons, and a global online learning algorithm to train them together. The method applies to complex domains in which some structure has to be recognized. This global problem is broken down into two layers of local subproblems: a filtering layer, which reduces the search space by identifying plausible phrase candidates, and a ranking layer, which discriminatively builds the optimal phrase structure. A recognitionbased feedback rule is presented which reflects to each local function its committed errors from a global point of view, and allows to train them together online as perceptrons. As a result, the learned functions automatically behave as filters and rankers, rather than binary classifiers, which we argue to be better for this type of problems. Extensive experimentation on partial parsing tasks gives state-of-the-art results and evinces the advantages of the global training method over optimizing each function locally, as in the traditional approach.

This work presents an architecture based on perceptrons to recognize phrase structures, and an online learning algorithm to train the perceptrons together and dependently. The recognition strategy applies learning in two layers: a filtering layer, which reduces the search space by identifying plausible phrase candidates, and a ranking layer, which recursively builds the optimal phrase structure. We provide a recognition-based feedback rule which reflects to each local function its committed errors from a global point of view, and allows to train them together online as perceptrons. Experimentation on a syntactic parsing problem, the recognition of clause hierarchies, improves state-of-the-art results and evinces the advantages of our global training method over optimizing each function locally and independently. 1

Machine learning applications often involve learning several different classifiers and combining their outcomes to a global decision in a way that provides a coherent inference that satisfies some constraints. This paper studies three general approaches to this problem concentrating on identifying sequential structure in the text. In all cases, the classifiers’ learning stage is decoupled from the inference stage. The first two models studied are Markovian approaches. One is a generative model that extends standard HMMs and the second is a conditional model; both allow the use of a rich observation structure and of general classifiers to model state-observation dependencies. The last model studied is an extension of constraint satisfaction formalisms. We develop efficient combination algorithms under all models and study them experimentally in the context of identifying the phrase structure of natural language sentences.

Parsing is a computationally intensive task due to the combinatorial explosion seen in chart parsing algorithms that explore possible parse trees. In this paper, we propose a method to limit the combinatorial explosion by restricting the CYK chart parsing algorithm based on the output of a chunk parser. When tested on the three parsers presented in (Collins, 1999), we observed an approximate three–fold speedup with only an average decrease of 0.17 % in both precision and recall.

This paper presents a Named Entity Extraction (NEE) system for the CoNLL-2003 shared task competition. As in the past year edition (Carreras et al., 2002a), we have approached the task by treating the two main sub--tasks of the problem, recognition (NER) and classification (NEC), sequentially and independently with separate modules. Both modules are machine learning based systems, which make use of binary and multiclass AdaBoost classifiers

This paper presents a Named Entity Extraction (NEE) system for the CoNLL-2003 shared task competition. As in the past year edition (Carreras et al., 2002a), we have approached the task by treating the two main sub--tasks of the problem, recognition (NER) and classification (NEC), sequentially and independently with separate modules. Both modules are machine learning based systems, which make use of binary and multiclass AdaBoost classifiers

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