Chinese Term Extraction Based on Delimiters

Terminology, 2007

Term extraction may be defined as a text mining activity whose main purpose is to obtain all the terms included in a text of a given domain. Since the eighties, and mainly due to the rapid scientific advances as well as the evolution of the communication systems, there has been a growing interest in obtaining the terms found in written documents. A number of techniques and strategies have been proposed for satisfying this requirement. At present it seems that term extraction has reached a maturity stage. Nevertheless, many of the systems proposed fail to qualitatively present their results, almost every system evaluates its abilities in an ad hoc manner (if any, many times). Often, the authors do not explain their evaluation methodology; therefore comparisons between different implementations are difficult to draw. In this paper, we review the state-of-the-art of term extraction systems evaluation in the framework of natural language systems evaluation. The main approaches are prese...

2009

Domain-specific terms provide vital semantic information for many natural language processing (NLP) tasks and applications, but remain a largely untapped resource in the field. In this paper, we propose an unsupervised method to extract domain-specific terms from the Reuters document collection using term frequency and inverse document frequency.

2021

Terminology extraction, also known as term extraction, is a subtask of information extraction. The goal of terminology extraction is to extract relevant words or phrases from a given corpus automatically. This paper focuses on the unsupervised automated domain term extraction method that considers chunking, preprocessing, and ranking domain-specific terms using relevance and cohesion functions for ICON 2020 shared task 2: TermTraction.

Terminology, 2007

Term extraction may be defined as a text mining activity whose main purpose is to obtain all the terms included in a text of a given domain. Since the eighties, and mainly due to the rapid scientific advances as well as the evolution of the communication systems, there has been a growing interest in obtaining the terms found in written documents. During this time, a number of techniques and strategies have been proposed for satisfying this requirement. At present it seems that term extraction has reached a maturity stage.

arXiv (Cornell University), 2023

Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. As units of knowledge in a specific field of expertise, extracted terms are not only beneficial for several terminographical tasks, but also support and improve several complex downstream tasks, e.g., information retrieval, machine translation, topic detection, and sentiment analysis. ATE systems, along with annotated datasets, have been studied and developed widely for decades, but recently we observed a surge in novel neural systems for the task at hand. Despite a large amount of new research on ATE, systematic survey studies covering novel neural approaches are lacking. We present a comprehensive survey of deep learning-based approaches to ATE, with a focus on Transformer-based neural models. The study also offers a comparison between these systems and previous ATE approaches, which were based on feature engineering and non-neural supervised learning algorithms.

2016

This chapter focuses on computational approaches to the automatic extraction of terms from domain specific corpora. The different subtasks of Automatic Term Extraction are presented in detail, including corpus compilation, unithood, termhood and variant detection, and system evaluation.

Proceedings of a workshop on held at Vienna, Virginia May 6-8, 1996 -, 1996

This paper provides a summary of the following topics: I. what was learned from porting the INQUERY information retrieval engine and the INFINDER term finder to Chinese 2. experiments at the University of Massachusetts evaluating INQUERY performance on Chinese newswire (Xinhua), 3. what was learned from porting selected components of PLUM to Chinese 4. experiments evaluating the POST part of speech tagger and named entity recognition on Chinese. 5. program issues in technology development.

1996

We argue that the important task of extracting domain and technical terms is much easier in Japanese than is commonly believed, and that technical term extraction should and can be more widely used. We present a method which shows that the implementation of such a tool for Japanese can be remarkably simple due to the regularity and rigidity of morphosyntactic characteristics of Japanese technical terms. Our learning algorithm bootstraps from the tagged output of a Japanese tokenizer/tagger to learn syntactic and morphological properties of technical terms. We show that very simple linguistic patterns are reliable enough to yield high precision technical terms from tagged texts, even for terms which occur only once and are usually overlooked by English technical term extractors. In addition, by incorporating a model for unknown words, we are able to extract correct technical terms from words not properly tagged.

The balancing act: Combining symbolic and statistical …, 1996

This paper presents an original method and its implementation to extract terminology from corpora by combining linguistic filters and statistical methods. Starting from a linguistic study of the terms of telecommunication domain, we designed a number of filters which enable us to obtain a first selection of sequences that may be considered as terms. Various statistical scores are applied to this selection and results are evaluated. This method has been applied to French and to English, but this paper deals only with French.

1999

Methods for multi-word term extraction generally involve statistical and/or linguistic techniques, but the linguistic information used is mainly in the form of simple syntactic information. Our approach makes use of semantic information from UMLS [12], a domain-specific thesaurus, and of a rich syntactic and semantic representation. by incorporation new contextual weights based on these, and in particular, a similarity measure we have developed, we improve on the NC-Value method of term recognition [6], which produces a ranking of candidate terms. Our results show that by adding deeper forms of information to this value, we can make more of a distinction between terms and non-terms, thereby improving the ranking, and we can also perform some disambiguation of these terms. 1