scholarly journals Using natural language patterns for the development of ontologies

2011 ◽  
pp. 211-230 ◽  
Author(s):  
Elena Montiel-Ponsoda ◽  
Guadalupe Aguado-de-Cea
Keyword(s):  
Natural Language ◽  
Language Patterns

A Unifying Translation of Natural Language Patterns to Object and Process Modeling

2011 ◽  
pp. 259-267
Author(s):  
Alexandra Galatescu
Keyword(s):  
Natural Language ◽  
Process Modeling ◽  
Graphical Representation ◽  
Semantic Networks ◽  
Logical Interpretation ◽  
Syntactic Knowledge ◽  
Language Patterns ◽  
Classical Semantic

The proposed translation of natural language (NL) patterns to object and process modeling is seen as an alternative to the symbolic notations, textual languages or classical semantic networks, the main representation tools today. Its necessity is motivated by the universality, unifying abilities, natural extensibility, logic and reusability of NL. The translation relies on a formalized, stylized and graphical representation of NL, bridging NL to an integrated view on the object and process modeling. Only the morphological and syntactic knowledge in NL is subject to translation, but the proposed solution anticipates the semantic and logical interpretation of a model. A brief presentation and exemplification of NL patterns in consideration precede the translation.

scholarly journals Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach (Preprint)

2019 ◽  
Author(s):  
Honghan Wu ◽  
Karen Hodgson ◽  
Sue Dyson ◽  
Katherine I Morley ◽  
Zina M Ibrahim ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Records ◽  
Free Text ◽  
Model Adaptation ◽  
Model Reuse ◽  
Language Patterns ◽  
Patient Profiles ◽  
Health Registry

BACKGROUND Much effort has been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records in order to construct comprehensive patient profiles for delivering better health care. Reusing NLP models in new settings, however, remains cumbersome, as it requires validation and retraining on new data iteratively to achieve convergent results. OBJECTIVE The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. METHODS We formally define and analyze the model adaptation problem in phenotype-mention identification tasks. We identify “duplicate waste” and “imbalance waste,” which collectively impede efficient model reuse. We propose a phenotype embedding–based approach to minimize these sources of waste without the need for labelled data from new settings. RESULTS We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% waste (duplicate waste), that is, phenotype mentions without the need for validation and model retraining and with very good performance (93%-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% waste (imbalance waste), that is, the effort required in “blind” model-adaptation approaches. CONCLUSIONS Adapting pretrained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.

scholarly journals A memory-based approach to learning shallow natural language patterns

1999 ◽  
Vol 11 (3) ◽  
pp. 369-390 ◽  
Author(s):  
Shlomo Argamon-Engelson ◽  
Ido Dagan ◽  
Yuval Krymolowski
Keyword(s):  
Natural Language ◽  
Approach To Learning ◽  
Language Patterns

scholarly journals Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach

10.2196/14782 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e14782 ◽  
Author(s):  
Honghan Wu ◽  
Karen Hodgson ◽  
Sue Dyson ◽  
Katherine I Morley ◽  
Zina M Ibrahim ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Records ◽  
Free Text ◽  
Model Adaptation ◽  
Model Reuse ◽  
Language Patterns ◽  
Patient Profiles ◽  
Health Registry

Background Much effort has been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records in order to construct comprehensive patient profiles for delivering better health care. Reusing NLP models in new settings, however, remains cumbersome, as it requires validation and retraining on new data iteratively to achieve convergent results. Objective The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. Methods We formally define and analyze the model adaptation problem in phenotype-mention identification tasks. We identify “duplicate waste” and “imbalance waste,” which collectively impede efficient model reuse. We propose a phenotype embedding–based approach to minimize these sources of waste without the need for labelled data from new settings. Results We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% waste (duplicate waste), that is, phenotype mentions without the need for validation and model retraining and with very good performance (93%-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% waste (imbalance waste), that is, the effort required in “blind” model-adaptation approaches. Conclusions Adapting pretrained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.

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