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1

A hybrid drug named entity recognition framework for real time pubmed data using deep learning and text summarization techniques

Mathur T., Raimond Kumudha, Deepakanmani S.

Przegląd Elektrotechniczny

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2023

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R. 99, nr 8

106--109

EN

Drug Named Entity Recognition (DNER) becomes indispensable for various medical relation extraction systems. Existing deep learning systems rely on the benchmark data for training as well as testing the model. However, it is very important to test on the real time data. In this research, we propose a hybrid DNER framework where we incorporate text summarization on real time data to create the test dataset. We have experimented with various text summarization techniques and found SciBERT model to give better results than other techniques.

PL

Rozpoznawanie jednostek o nazwie leku (DNER) staje się nieodzowny dla innych systemów ekstrakcji relacji medycznych. Istniejące systemy głębokiego uczenia się opierają się na danych porównawczych zarówno podczas szkolenia, jak i testowania modelu. Jednak bardzo ważne jest, aby testować dane w czasie rzeczywistym. W tym badaniu proponujemy hybrydową strukturę DNER, w której uwzględniamy podsumowanie tekstu na danych w czasie rzeczywistym w celu utworzenia zestawu danych testowych. Eksperymentowaliśmy z różnymi technikami podsumowania tekstu i stwierdziliśmy, że model BERT daje lepsze wyniki niż inne techniki.

2

Automatic extractive and generic document summarization based on NMF

Aghdam Mehdi Hosseinzadeh

Journal of Artificial Intelligence and Soft Computing Research

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2023

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Vol. 13, No. 1

37--49

EN

Nowadays, textual information grows exponentially on the Internet. Text summarization (TS) plays a crucial role in the massive amount of textual content. Manual TS is time-consuming and impractical in some applications with a huge amount of textual information. Automatic text summarization (ATS) is an essential technology to overcome mentioned challenges. Non-negative matrix factorization (NMF) is a useful tool for extracting semantic contents from textual data. Existing NMF approaches only focus on how factorized matrices should be modeled, and neglect the relationships among sentences. These relationships provide better factorization for TS. This paper suggests a novel non-negative matrix factorization for text summarization (NMFTS). The proposed ATS model puts regularizes on pairwise sentences vectors. A new cost function based on the Frobenius norm is designed, and an algorithm is developed to minimize this function by proposing iterative updating rules. The proposed NMFTS extracts semantic content by reducing the size of documents and mapping the same sentences closely together in the latent topic space. Compared with the basic NMF, the convergence time of the proposed method does not grow. The convergence proof of the NMFTS and empirical results on the benchmark data sets show that the suggested updating rules converge fast and achieve superior results compared to other methods.

3

An Insight Into The Z-number Approach To CWW

Pal S.K., Banerjee R., Dutta S., Sarma S. S.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

197--229

EN

The Z-number is a new fuzzy-theoretic concept, proposed by Zadeh in 2011. It extends the basic philosophy of Computing With Words (CWW) to include the perception of uncertainty of the information conveyed by a natural language statement. The Z-number thus, serves as a model of linguistic summarization of natural language statements, a technique to merge human-affective perspectives with CWW, and consequently can be envisaged to play a radical role in the domain of CWW-based system design and Natural Language Processing (NLP). This article presents a comprehensive investigation of the Z-number approach to CWW. We present here: a) an outline of our understanding of the generic architecture, algorithm and challenges underlying CWW in general; b) a detailed study of the Z-number methodology - where we propose an algorithm for CWW using Z-numbers, define a Z-number based operator for the evaluation of the level of requirement satisfaction, and describe simulation experiments of CWW utilizing Z-numbers; and c) analyse the strengths and the challenges of the Z-numbers, and suggest possible solution strategies. We believe that this article would inspire research on the need for inclusion of human-behavioural aspects into CWW, as well as the integration of CWW and NLP.

4

Sentence extraction using similar words

Suzuki Y., Fukumoto F.

Archives of Control Sciences

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2005

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Vol. 15, no. 4

665-672

EN

In both written and spoken languages, we sometimes use different words in order to describe the same thing. For Instance, we use "candidacy and "running in an election" as the same meaning. This makes text classification, event tracking and text summarization difficult. In this paper, we propose a method to extract words which are semantically similar to each other accurately. Using this method, we extracted similar word pairs from newspaper articles. Furthermore, we performed sentence extraction from the newspaper articles using the extracted similar word pairs. We hypothesized that the headline is salient information of the newspaper article and presence of headline terms in the article can be used to detect salient sentences of news text. By using similar words in the headline, we obtained better results than that without using it. The results suggest that our method is useful for text summarization.