Extracting Chinese-English Bilingual Core Terminology from Parallel Classified Corpora in Special Domain

Zhang, Chengzhi

doi:10.1109/wi-iat.2009.280

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…In addition, while any task like the one we will introduce here tackles the problem of knowledge acquisition and tries to engineer the bottleneck of knowledge acquisition through automated methodologies and algorithms, the development and evaluation of such methods relies closely on the provided dataset for testing and training e.g. [9], [10]. In other words such research is more task-driven rather than fact-driven.…”

Section: Introductionmentioning

confidence: 99%

Towards Technology Structure Mining from Scientific Literature

QasemiZadeh

2010

Lecture Notes in Computer Science

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Abstract. This paper introduces the task of Technology-Structure Mining to support Management of Technology. We propose a linguistic based approach for identification of Technology Interdependence through extraction of technology concepts and relations between them. In addition, we introduce Technology Structure Graph for the task formalization. While the major challenge in technology structure mining is the lack of a benchmark dataset for evaluation and development purposes, we describes steps that we have taken towards providing such a benchmark. The proposed approach is initially evaluated and applied in the domain of Human Language Technology and primarily results are demonstrated. We further explain plans and research challenges for evaluation of the proposed task.

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Section: Introductionmentioning

confidence: 99%

Towards Technology Structure Mining from Scientific Literature

QasemiZadeh

2010

Lecture Notes in Computer Science

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“…While any task like the one we will introduce here tackles the problem of knowledge acquisition and tries to engineer solutions to the bottleneck of knowledge acquisition through automated methodologies and algorithms, the development and evaluation of such methods relies closely on the provided dataset for testing and training e.g. [10], [11]. In addition, understanding and evaluation of the outcome of an IE/OL task is subject to the understanding of domain experts and the sort of information they are looking for; generally speaking, these activities are more task-driven rather than fact-driven.…”

Section: Introductionmentioning

confidence: 99%

Developing a Dataset for Technology Structure Mining

QasemiZadeh

Buitelaar

Monaghan

2010

2010 IEEE Fourth International Conference on Semantic Computing

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This paper describes steps that have been taken to construct a development dataset for the task of Technology Structure Mining. We have defined the proposed task as the process of mapping a scientific corpus into a labeled digraph named a Technology Structure Graph as described in the paper. The generated graph expresses the domain semantics in terms of interdependencies between pairs of technologies that are named (introduced) in the target scientific corpus. The dataset comprises a set of sentences extracted from the ACL Anthology Corpus. Each sentence is annotated with at least two technologies in the domain of Human Language Technology and the interdependence between them. The annotations-technology markup and their interdependencies-are expressed at two layers: lexical and termino-conceptual. Lexical representation of technologies comprises varying lexicalizations of a technology. However, at the termino-conceptual layer all these lexical variations refer to the same concept. We have adopted the same approach for representing Semantic Relations; at the lexical layer a semantic relation is a predicate i.e. defined based on the sentence surface structure; however at the termino-conceptual layer semantic relations are classified into conceptual relations either taxonomic or non-taxonomic. Morover, the contexts that interdependencies are extracted from are classified into five groups based on the linguistic criteria and syntactic structure that are identified by the human annotators. The dataset initially comprises of 482 sentences. We hope this effort results in a benchmark that can be used for the technology structure mining task as defined in the paper.

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Extracting Chinese-English Bilingual Core Terminology from Parallel Classified Corpora in Special Domain

Cited by 2 publications

References 12 publications

Towards Technology Structure Mining from Scientific Literature

Towards Technology Structure Mining from Scientific Literature

Developing a Dataset for Technology Structure Mining

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