1,078 research outputs found
Automatic annotation of the Penn-treebank with LFG f-structure information
Lexical-Functional Grammar f-structures are abstract syntactic representations approximating basic predicate-argument structure. Treebanks annotated with f-structure information are required as training resources for stochastic versions of unification and constraint-based
grammars and for the automatic extraction of such resources. In a number of papers (Frank, 2000; Sadler, van Genabith and Way, 2000) have developed methods for automatically annotating treebank resources with f-structure information. However, to date, these methods
have only been applied to treebank fragments of the order of a few hundred trees. In the present paper we present a new method that scales and has been applied to a complete treebank, in our case the WSJ section of Penn-II (Marcus et al, 1994), with more than 1,000,000 words in about 50,000 sentences
Stabilizing knowledge through standards - A perspective for the humanities
It is usual to consider that standards generate mixed feelings among
scientists. They are often seen as not really reflecting the state of the art
in a given domain and a hindrance to scientific creativity. Still, scientists
should theoretically be at the best place to bring their expertise into
standard developments, being even more neutral on issues that may typically be
related to competing industrial interests. Even if it could be thought of as
even more complex to think about developping standards in the humanities, we
will show how this can be made feasible through the experience gained both
within the Text Encoding Initiative consortium and the International
Organisation for Standardisation. By taking the specific case of lexical
resources, we will try to show how this brings about new ideas for designing
future research infrastructures in the human and social sciences
Towards Comprehensive Computational Representations of Arabic Multiword Expressions
A successful computational treatment of multiword expressions (MWEs) in natural languages leads to a robust NLP system which considers the long-standing problem of language ambiguity caused primarily by this complex linguistic phenomenon. The first step in addressing
this challenge is building an extensive reliable MWEs language resource LR with comprehensive computational representations across all linguistic levels. This forms the cornerstone in understanding the heterogeneous linguistic behaviour of MWEs in their various manifestations. This paper presents a detailed framework for computational representations of Arabic MWEs (ArMWEs) across all linguistic levels based on the state-of-the-art lexical mark-up framework (LMF) with the necessary modifications to suit the distinctive properties of Modern Standard Arabic (MSA). This work forms part of a larger project that aims to develop a comprehensive computational lexicon of ArMWEs for NLP and language pedagogy LP (JOMAL project)
Towards Comprehensive Computational Representations of Arabic Multiword Expressions
A successful computational treatment of multiword expressions (MWEs) in natural languages leads to a robust NLP system which considers the long-standing problem of language ambiguity caused primarily by this complex linguistic phenomenon. The first step in addressing
this challenge is building an extensive reliable MWEs language resource LR with comprehensive computational representations across all linguistic levels. This forms the cornerstone in understanding the heterogeneous linguistic behaviour of MWEs in their various manifestations. This paper presents a detailed framework for computational representations of Arabic MWEs (ArMWEs) across all linguistic levels based on the state-of-the-art lexical mark-up framework (LMF) with the necessary modifications to suit the distinctive properties of Modern Standard Arabic (MSA). This work forms part of a larger project that aims to develop a comprehensive computational lexicon of ArMWEs for NLP and language pedagogy LP (JOMAL project)
MBT: A Memory-Based Part of Speech Tagger-Generator
We introduce a memory-based approach to part of speech tagging. Memory-based
learning is a form of supervised learning based on similarity-based reasoning.
The part of speech tag of a word in a particular context is extrapolated from
the most similar cases held in memory. Supervised learning approaches are
useful when a tagged corpus is available as an example of the desired output of
the tagger. Based on such a corpus, the tagger-generator automatically builds a
tagger which is able to tag new text the same way, diminishing development time
for the construction of a tagger considerably. Memory-based tagging shares this
advantage with other statistical or machine learning approaches. Additional
advantages specific to a memory-based approach include (i) the relatively small
tagged corpus size sufficient for training, (ii) incremental learning, (iii)
explanation capabilities, (iv) flexible integration of information in case
representations, (v) its non-parametric nature, (vi) reasonably good results on
unknown words without morphological analysis, and (vii) fast learning and
tagging. In this paper we show that a large-scale application of the
memory-based approach is feasible: we obtain a tagging accuracy that is on a
par with that of known statistical approaches, and with attractive space and
time complexity properties when using {\em IGTree}, a tree-based formalism for
indexing and searching huge case bases.} The use of IGTree has as additional
advantage that optimal context size for disambiguation is dynamically computed.Comment: 14 pages, 2 Postscript figure
OER Development and Promotion. Outcomes of an International Research Project on the OpenCourseWare Model
In this paper, we describe the successful results of an international research project focused on the use of Web technology in the educational context. The article explains how this international project, funded by public organizations and developed over the last two academic years, focuses on the area of open educational resources (OER) and particularly the educational content of the OpenCourseWare (OCW) model. This initiative has been developed by a research group composed of researchers from three countries. The project was enabled by the Universidad Politécnica de Madrid OCW Office�s leadership of the Consortium of Latin American Universities and the distance education know-how of the Universidad Técnica Particular de Loja (UTPL, Ecuador). We give a full account of the project, methodology, main outcomes and validation. The project results have further consolidated the group, and increased the maturity of group members and networking with other groups in the area. The group is now participating in other research projects that continue the lines developed her
Investigating and extending the methods in automated opinion analysis through improvements in phrase based analysis
Opinion analysis is an area of research which deals with the computational treatment of opinion statement and subjectivity in textual data. Opinion analysis has emerged over the past couple of decades as an active area of research, as it provides solutions to the issues raised by information overload. The problem of information overload has emerged with the advancements in communication technologies which gave rise to an exponential growth in user generated subjective data available online. Opinion analysis has a rich set of applications which are used to enable opportunities for organisations such as tracking user opinions about products, social issues in communities through to engagement in political participation etc.The opinion analysis area shows hyperactivity in recent years and research at different levels of granularity has, and is being undertaken. However it is observed that there are limitations in the state-of-the-art, especially as dealing with the level of granularities on their own does not solve current research issues. Therefore a novel sentence level opinion analysis approach utilising clause and phrase level analysis is proposed. This approach uses linguistic and syntactic analysis of sentences to understand the interdependence of words within sentences, and further uses rule based analysis for phrase level analysis to calculate the opinion at each hierarchical structure of a sentence. The proposed opinion analysis approach requires lexical and contextual resources for implementation. In the context of this Thesis the approach is further presented as part of an extended unifying framework for opinion analysis resulting in the design and construction of a novel corpus. The above contributions to the field (approach, framework and corpus) are evaluated within the Thesis and are found to make improvements on existing limitations in the field, particularly with regards to opinion analysis automation. Further work is required in integrating a mechanism for greater word sense disambiguation and in lexical resource development
Parsing with PCFGs and automatic f-structure annotation
The development of large coverage, rich unification- (constraint-) based grammar resources is very time consuming, expensive and requires lots of linguistic expertise. In this paper we report initial results on a new methodology that attempts to partially automate the development of substantial parts of large coverage, rich unification- (constraint-) based grammar resources. The method is based on a treebank resource (in our case Penn-II) and an automatic f-structure annotation algorithm that annotates treebank trees with proto-f-structure information. Based on these, we present two parsing architectures: in our pipeline architecture we first extract a PCFG from the treebank following the method of (Charniak,1996), use the PCFG to parse new text, automatically annotate the resulting trees with our f-structure annotation algorithm and generate proto-f-structures. By contrast, in the integrated architecture we first automatically annotate the treebank trees with f-structure information and then extract an annotated PCFG (A-PCFG) from the treebank. We then use the A-PCFG to parse new text to generate proto-f-structures. Currently our best parsers achieve more than 81% f-score on the 2400 trees in section 23 of the Penn-II treebank and more than 60% f-score on gold-standard proto-f-structures for 105 randomly selected trees from section 23
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