Rewiev of current text representation technics for semantic relationship extraction

2021 ◽  
Vol 0 (11-12/2020) ◽  
pp. 13-22
Author(s):  
Michał Gałusza

Article provides review on current most popular text processing technics; sketches their evolution and compares sequence and dependency models in detecting semantic relationship between words.

2020 ◽  
Vol 34 (05) ◽  
pp. 9579-9586
Author(s):  
Xiao Zhang ◽  
Dejing Dou ◽  
Ji Wu

External knowledge is often useful for natural language understanding tasks. We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which incorporates structured knowledge into text representations. Unlike entity embedding methods, our approach encodes a knowledge graph into a context model. CC embeddings can be easily reused for a wide range of tasks in a similar fashion to pre-trained language models. Our model effectively encodes the huge UMLS database by leveraging semantic generalizability. Experiments on electronic health records (EHRs) and medical text processing benchmarks showed our model gives a major boost to the performance of supervised medical NLP tasks.


2020 ◽  
pp. 071-080
Author(s):  
O.P. Zhezherun ◽  
◽  
O.R. Smysh ◽  
◽  

The article focuses on developing a software solution for solving planimetry problems that are written in Ukrainian. We discuss tendencies and available abilities in Ukrainian natural language processing. Presenting a comprehensive analysis of different types of describing a problem, which shows regularities in the formulation and structure of the text representation of problems. Also, we demonstrate the similarities of writing a problem not only in Ukrainian but also in Belarusian, English, and Russian languages. The final result of the paper is a system that uses the morphosyntactic analyzer to process a problem’s text and provide the answer to it. Ukrainian natural language processing is growing rapidly and showing impressive results. Huge possibilities appear as the Gold standard annotated corpus for Ukrainian language was recently developed. The created architecture is flexible, which indicates the possibility of adding both new geometry figures and their properties, as well as the additional logic to the program. The developed system with a little reformatting can be used with other natural languages, such as English, Belarusian or Russian, as the algorithm for text processing is universal due to the globally accepted representations for presenting such types of mathematical problems. Therefore, the further development of the system is possible.


2004 ◽  
Vol 13 (01) ◽  
pp. 141-156 ◽  
Author(s):  
CHENG NIU ◽  
WEI LI ◽  
JIHONG DING ◽  
ROHINI K. SRIHARI

One challenge in text processing is the treatment of case insensitive documents such as speech recognition results. The traditional approach is to re-train a language model excluding case-related features. This paper presents an alternative two-step approach whereby a preprocessing module (Step 1) is designed to restore case-sensitive form which is subsequently processed by the original system (Step 2). Step 1 is mainly implemented as a Hidden Markov Model trained on a large raw corpus of case sensitive documents. It is demonstrated that this approach (i) outperforms the feature exclusion approach for named entity tagging, (ii) leads to limited degradation for parsing, relationship extraction and case insensitive question answering, (iii) reduces system complexity, and (iv) has wide applicability: the restored text can be used in both statistical model and rule-based systems.


2016 ◽  
Vol 23 (6) ◽  
pp. 826-840 ◽  
Author(s):  
N. S. Lagutina ◽  
K. V. Lagutina ◽  
E. I. Mamedov ◽  
I. V. Paramonov

2001 ◽  
Author(s):  
Robert J. Hines ◽  
Mark A. McDaniel ◽  
Melissa Guynn

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