A Distributed Approach to Coreference Resolution in Multiagent Text Analysis for Ontology Population

Garanina, Natalia Olegovna; Sidorova, Elena A.; Kononenko, Irina

doi:10.1007/978-3-319-74313-4_12

Cited by 2 publications

(6 citation statements)

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“…Ontology population is the methodologies for extracting the instances of concepts from the Natural Language text in the external source, then adding them to the existing domain ontology [16][17][18]. There are several approaches to perform ontology population, i.e.…”

Section: Ontology Population Techniquesmentioning

confidence: 99%

New instances classification framework on Quran ontology applied to question answering system

2019

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Instances classification with the small dataset for Quran ontology is the current research problem which appears in Quran ontology development. The existing classification approach used machine learning: Backpropagation Neural Network. However, this method has a drawback; if the training set amount is small, then the classifier accuracy could decline. Unfortunately, Holy Quran has a small corpus. Based on this problem, our study aims to formulate new instances classification framework for small training corpus applied to semantic question answering system. As a result, the instances classification framework consists of several essential components: pre-processing, morphology analysis, semantic analysis, feature extraction, instances classification with Radial Basis Function Networks algorithm, and the transformation module. This algorithm is chosen since it robustness to noisy data and has an excellent achievement to handle small dataset. Furthermore, document processing module on question answering system is used to access instances classification result in Quran ontology.

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Section: Ontology Population Techniquesmentioning

confidence: 99%

New instances classification framework on Quran ontology applied to question answering system

2019

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“…3. The main analysis module constructs objects, corresponding to instances of subject domain ontology, from the terms [4], and resolves coreference [6]. The input for this module is the terminological cover with the structural information from the segment cover, and the analysis rules which implement semantic and syntactic models and ontology population rules.…”

Section: The Segmentator Module Performs Segmentationmentioning

confidence: 99%

“…The higher class agents use results of constructing conflict-free groups from the lower class agents. The details of the coreference resolution process are described in [6], where we use a simpler semantic measure of coreference similarity in the task 4 and the computation of the measure is not discussed. Here we use more complex and precise semantic measure, so it is reasonable to describe its computation in detail.…”

Section: The Segmentator Module Performs Segmentationmentioning

confidence: 99%

“…We use the proposed framework to improve our coreference resolution algorithm suggested in [6] for making the decision on the candidate admissibility, which is used in our general approach to text analysis and information extraction for populating subject domain ontology. In our approach, the following IE tasks are performed: the preliminary extraction of subject domain terms from a given text [14]; the segmentation of the text into formal and genre fragments (sentences, sections, headlines, etc) [22]; the construction of objects corresponding to instances of a subject domain ontology, from the terms [4] and the coreference resolution [6]; the lexical and syntactic disambiguation [5]; the update of the ontology with the processed objects (planned as future work). In our framework, the coreference resolution problem means detecting if some group of retrieved objects refers to the particular ontology instance.…”

Section: Introductionmentioning

confidence: 99%

“…Our approach to coreference resolution [6] is rule-based, because we deal with limited subject domains. Our proposed algorithm is ontology-driven as it strongly relies on the structure of the underlying predefined domain ontology.…”

Section: Introductionmentioning

confidence: 99%

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Using Multiple Semantic Measures for Coreference Resolution in Ontology Population

Garanina¹,

Sidorova²,

Kononenko³

et al. 2017

IJC

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The problem of populating an ontology consists in adding to it some new, domain-specific content from an input expressed, in particular, in a natural language. We focus on an important aspect in the ontology population process – finding and resolving coreferences, i.e., similar mentions of entities in the input text. Our contribution is a novel formal framework that extends the state-of-the-art approaches to coreference resolution by using multiple semantic similarity properties in the resolution process, i.e., we extend the list of the ontological properties used for coreference resolution with additional properties such as inverse, symmetry, intersection, union, etc. We use the proposed framework to improve our previously proposed algorithm for coreference resolution used in our general approach to text analysis and information extraction for populating subject domain ontologies. We describe a multi-agent implementation of our information extraction system and we show that using additional semantic similarity measures for evaluating coreferential candidates improves the quality of the coreference resolution process, especially for complex objects whose coreferencing has not been yet studied in detail.

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A Distributed Approach to Coreference Resolution in Multiagent Text Analysis for Ontology Population

Cited by 2 publications

References 12 publications

New instances classification framework on Quran ontology applied to question answering system

New instances classification framework on Quran ontology applied to question answering system

Using Multiple Semantic Measures for Coreference Resolution in Ontology Population

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