Shalmaneser–a toolchain for shallow semantic parsing (original) (raw)

Semantic parsing based on framenet

2004

This paper describes our method based on Support Vector Machines for automatically assigning semantic roles to constituents of English sentences. This method employs four different feature sets, one of which being first reported herein. The combination of features as well as the extended training data we considered have produced in the Senseval-3 experiments an F1-score of 92.5% for the unrestricted case and of 76.3% for the restricted case. 1 The second classification represents the detection of role boundaries. The semantic parsing defined as two different classification tasks was introduced in (Gildea and Jurasfky, 2002).

A General Purpose FrameNet-based Shallow Semantic Parser

2010

In this paper we present a new FrameNet-based Shallow Semantic Parser. While Shallow Semantic Parsing has been a popular Natural Language Processing task since the 2004 and 2005 CoNLL Shared Task editions, efforts in extending such task to the FrameNet setting have been constrained by practical software engineering issues. We hereby analyze these issues, identify desirable requirements for a practical parsing framework, and show the results of our software implementation. In particular, we attempt at meeting requirements arising from both a) the need of a flexible environment supporting current ongoing research, and b) the willingness of providing an effective platform supporting preliminary application prototypes in the field. After introducing the task of FrameNet-based Shallow Semantic Parsing, we sketch the system processing workflow and summarize a set of successful experimental results, directing the reader to previous published papers for extended experiment descriptions and wider discussion of the achieved results.

Semantic role labeling using different syntactic views

Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05, 2005

Semantic role labeling is the process of annotating the predicate-argument structure in text with semantic labels. In this paper we present a state-of-the-art baseline semantic role labeling system based on Support Vector Machine classifiers. We show improvements on this system by: i) adding new features including features extracted from dependency parses, ii) performing feature selection and calibration and iii) combining parses obtained from semantic parsers trained using different syntactic views. Error analysis of the baseline system showed that approximately half of the argument identification errors resulted from parse errors in which there was no syntactic constituent that aligned with the correct argument. In order to address this problem, we combined semantic parses from a Minipar syntactic parse and from a chunked syntactic representation with our original baseline system which was based on Charniak parses. All of the reported techniques resulted in performance improvements. *

Shallow semantic parsing based on FrameNet, VerbNet and PropBank

2006

This article describes a semantic parser based on FrameNet semantic roles that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. We link the above resources through a mapping between Intersective Levin classes, which are part of PropBank's annotation, and the FrameNet frames. By using Levin classes, we successfully detect FrameNet semantic roles without relying on the frame information. At the same time, the combined usage of the above resources increases the verb coverage and confers more robustness to our parser. The experiments with Support Vector Machines on automatic Levin class detection suggest that (a) tree kernels are well suited for the task and (b) Intersective Levin classes can be used to improve the accuracy of semantic parsing based on FrameNet roles.

Semantic Classes in CESS-LEX: Semantic Annotation of CESS-ECE1

Annotated corpora constitute a crucial resource to acquire or induce linguistic knowledge about how languages are used. In this sense, it is widely admitted that tagged corpora appear to be a very useful resource for computational and linguistic analysis of languages. The more explicit linguistic information they contain, the more interesting and useful they are. In this paper we present the theoretical basis for semantic annotation of two treebanks, CESS-ESP and CESS-CAT, focusing specially on the verbal semantic classes that determine the mapping between syntactic functions and semantic roles.

Semantic parsing for high-precision semantic role labelling

Proceedings of the Twelfth Conference on Computational Natural Language Learning - CoNLL '08, 2008

In this paper, we report experiments that explore learning of syntactic and semantic representations. First, we extend a state-of-the-art statistical parser to produce a richly annotated tree that identifies and labels nodes with semantic role labels as well as syntactic labels. Secondly, we explore rule-based and learning techniques to extract predicate-argument structures from this enriched output. The learning method is competitive with previous single-system proposals for semantic role labelling, yields the best reported precision, and produces a rich output. In combination with other high recall systems it yields an F-measure of 81%.

FreeLing 1.3: Syntactic and semantic services in an open-source NLP library

This paper describes version 1.3 of the FreeLing suite of NLP tools. FreeLing was first released in February 2004 providing morpholog-ical analysis and PoS tagging for Catalan, Spanish, and English. From then on, the package has been improved and enlarged to cover more languages (i.e. Italian and Galician) and offer more services: Named entity recognition and classification, chunking, dependency parsing, and WordNet based semantic annotation. FreeLing is not conceived as end-user oriented tool, but as library on top of which powerful NLP applications can be developed. Nev-ertheless, sample interface programs are provided, which can be straightforwardly used as fast, flexible, and efficient corpus processing tools. A remarkable feature of FreeLing is that it is distributed under a free-software LGPL license, thus enabling any developer to adapt the package to his needs in order to get the most suitable behaviour for the application being developed.

A pipeline approach for syntactic and semantic dependency parsing

Proceedings of the …, 2008

This paper describes our system for syntactic 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: Syntactic dependency parsing is performed by a tournament model with a support vector machine; word sense disambiguation is performed by a nearest neighbour method in a compressed feature space by probabilistic latent semantic indexing; and semantic role labeling is performed by a an online passive-aggressive algorithm. The submitted result was 79.10 macroaverage F1 for the joint task, 87.18% syntactic dependencies LAS, and 70.84 semantic dependencies F1. After the deadline, we constructed the other configuration, which achieved 80.89 F1 for the joint task, and 74.53 semantic dependencies F1. The result shows that the configuration of pipeline is a crucial issue in the task.