Articles

This article investigates the effect of a set of linguistically motivated features on ar- gument disambiguation in data-driven de- pendency parsing of Swedish. We present results from experiments with gold stan- dard features, such as animacy, definite- ness and finiteness, as well as correspond- ing experiments where these features have …...

A semantic parser learning system learns to map natural language sentences into their domain-specific formal meaning rep- resentations, but if the constructs of the meaning representation language do not correspond well with the natural language then the system may not learn a good se- mantic parser. This paper presents ap- …...

Combination of features contributes to a significant improvement in accuracy on tasks such as part-of-speech (POS) tag- ging and text chunking, compared with us- ing atomic features. However, selecting combination of features on learning with large-scale and feature-rich training data requires long training time. We propose a fast boosting-based algorithm …...

We describe a parsing approach that makes use of the perceptron algorithm, in conjunction with dynamic programming methods, to recover full constituent-based parse trees. The formalism allows a rich set of parse-tree features, including PCFG- based features, bigram and trigram dependency fea- tures, and surface features. A severe challenge in …...

In this paper, we report experiments that explore learning of syntactic and seman- tic representations. First, we extend a state-of-the-art statistical parser to pro- duce a richly annotated tree that identi- fies and labels nodes with semantic role la- bels as well as syntactic labels. Secondly, we explore rule-based and …...

This paper proposes a novel method to analyze syntactic dependencies and label semantic dependencies around both the verbal predicates and the nouns. In this method, a probabilistic model is designed to obtain a global optimal result. More- over, a predicate identification model and a disambiguation model are proposed to label …...

This paper proposes a dependency tree- based SRL system with proper pruning and extensive feature engineering. Official evaluation on the CoNLL 2008 shared task shows that our system achieves 76.19 in la- beled macro F1 for the overall task, 84.56 in labeled attachment score for syntactic dependencies, and 67.12 in …...

Our system for the CoNLL 2008 shared task uses a set of individual parsers, a set of stand-alone semantic role labellers, and a joint system for parsing and semantic role labelling, all blended together. The system achieved a macro averaged labelled F1- score of 79.79 (WSJ 80.92, Brown 70.49) for …...

This paper describes a system to solve the joint learning of syntactic and seman- tic dependencies. An directed graphical model is put forward to integrate depen- dency relation classification and semantic role labeling. We present a bilayer di- rected graph to express probabilistic re- lationships between syntactic and seman- tic …...

In this paper, we describe a syntactic and semantic dependency parsing system sub- mitted to the shared task of CoNLL 2008. The proposed system consists of five mod- ules: syntactic dependency parser, predi- cate identifier, local semantic role labeler, global role sequence candidate generator, and role sequence selector. The syntac- …...

This paper describes our system for syn- tactic and semantic dependency parsing to participate the shared task of CoNLL- 2008. We use a pipeline approach, in which syntactic dependency parsing, word sense disambiguation, and semantic role labeling are performed separately: Syn- tactic dependency parsing is performed by a tournament model …...

This paper describes the two algorithms we developed for the CoNLL 2008 Shared Task “Joint learning of syntactic and se- mantic dependencies”. Both algorithms start parsing the sentence using the same syntactic parser. The first algorithm uses machine learning methods to identify the semantic dependencies in four stages: identification and …...

A Maximum Entropy Model based system for discriminative learning of syntactic and semantic dependencies submitted to the CoNLL-2008 shared task (Surdeanu, et al., 2008) is presented in this paper. The sys- tem converts the dependency learning task to classification issues and reconstructs the dependent relations based on classification results. Finally …...

We describe the system submitted to the closed challenge of the CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies. Syntactic de- pendencies are processed with the Malt- Parser 0.4. Semantic dependencies are processed with a combination of memory- based classifiers. The system achieves 78.43 labeled macro F1 …...

This paper describes our system to carry out the joint parsing of syntactic and se- mantic dependencies for our participation in the shared task of CoNLL-2008. We il- lustrate that both syntactic parsing and se- mantic parsing can be transformed into a word-pair classification problem and im- plemented as a …...

In this paper we present our syntactic and semantic dependency parsing system par- ticipated in both closed and open compe- titions of the CoNLL 2008 Shared Task. By combining the outcome of two state-of- the-art syntactic dependency parsers, we achieved high accuracy in syntactic de- pendencies (87.32%). With MRSes from …...

This paper describes a system that jointly parses syntactic and semantic dependen- cies, presented at the CoNLL-2008 shared task (Surdeanu et al., 2008). It combines online Peceptron learning (Collins, 2002) with a parsing model based on the Eisner algorithm (Eisner, 1996), extended so as to jointly assign syntactic and semantic …...

This paper presents our contribution in the closed track of the 2008 CoNLL Shared Task (Surdeanu et al., 2008). To tackle the problem of joint syntactic–semantic anal- ysis, the system relies on a syntactic and a semantic subcomponent. The syntactic model is a bottom-up projective parser us- ing pseudo-projective transformations, …...

We propose a solution to the challenge of the CoNLL 2008 shared task that uses a generative history-based latent variable model to predict the most likely derivation of a synchronous dependency parser for both syntactic and semantic dependencies. The submitted model yields 79.1% macro- average F1 performance, for the joint …...

The Conference on Computational Natu- ral Language Learning is accompanied ev- ery year by a shared task whose purpose is to promote natural language processing applications and evaluate them in a stan- dard setting. In 2008 the shared task was dedicated to the joint parsing of syntactic and semantic dependencies. …...