Low-Bias Extraction of Domain-Specific Concepts
نویسنده
چکیده
The availability of domain-specific knowledge models in various forms has led to the development of several tools and applications specialized on complex domains such as bio-medecine, tourism and chemistry. Yet, most of the current approaches to the extraction of domain-specific knowledge from text are limited in their portability to other domains and languages. In this paper, we present and evaluate an approach to the low-bias extraction of domain-specific concepts. Our approach is based on graph clustering and makes no use of a-priori knowledge about the language or the domain to process. Therefore, it can be used on virtually any language. The evaluation is carried out on two data sets of different cleanness and size.
منابع مشابه
Combining Syntax & Ontologies for Information Extraction
This paper presents an information extraction system, dedicated to message filtering for a specific domain (security systems). The paper focuses on a method for identifying domain-specific ontology elements (terms and concepts), using syntactic information and an existing domain ontology. The domain ontology is represented using description logics. The system uses description logics inference m...
متن کاملSelecting Domain-Specific Concepts for Question Generation With Lightly-Supervised Methods
In this paper we propose content selection methods for question generation (QG) which exploit domain knowledge. Traditionally, QG systems apply syntactical transformation on individual sentences to generate open domain questions. We hypothesize that a QG system informed by domain knowledge can ask more important questions. To this end, we propose two lightly-supervised methods to select salient...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملL-ISA: Learning Domain Specific Isa-Relations from the Web
Automated extraction of ontological knowledge from text corpora is a relevant task in Natural Language Processing. In this paper, we focus on the problem of finding hypernyms for relevant concepts in a specific domain (e.g. Optical Recording) in the context of a concrete and challenging application scenario (patent processing). To this end information available on the Web is exploited. The extr...
متن کاملA protocol for constructing a domain-specific ontology for use in biomedical information extraction using lexical-chaining analysis
In order to do more semantics-based information extraction, we require specialized domain models. We develop a hybrid approach for constructing such a domain-specific ontology, which integrates key concepts from the protein-protein– interaction domain with the Gene Ontology. In addition, we present a method for using the domain-specific ontology in a discourse-based analysis module for analyzin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009