scholarly journals LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction

2021 ◽  
Vol 11 (16) ◽  
pp. 7237
Author(s):  
Pengjun Zhai ◽  
Chen Wang ◽  
Yu Fang
Keyword(s):  
Pattern Matching ◽  
Extraction Methods ◽  
Event Extraction ◽  
Diagnosis And Treatment ◽  
Data Set ◽  
Generalization Ability ◽  
Word Level ◽  
Sentence Level ◽  
Simplex Model ◽  
Semantic Dependency

Most existing medical event extraction methods have primarily adopted a simplex model based on either pattern matching or deep learning, which ignores the distribution characteristics of entities and events in the medical corpus. They have not categorized the granularity of event elements, leading to the poor generalization ability of the model. This paper proposes a diagnosis and treatment event extraction method in the Chinese language, fusing long short-level semantic dependency of the corpus, LSLSD, for solving these problems. LSLSD can effectively capture different levels of semantic information within and between event sentences in the electronic medical record (EMR) corpus. Moreover, the event arguments are divided into short word-level and long sentence-level, with the sequence annotation and pattern matching combined to realize multi-granularity argument recognition, as well as to improve the generalization ability of the model. Finally, this paper constructs a diagnosis and treatment event data set of Chinese EMRs by proposing a semi-automatic corpus labeling method, and an enormous number of experiment results show that LSLSD can improve the F1-value of event extraction task by 7.1% compared with the several strong baselines.

scholarly journals Extraction of Relying Factors to be Diabetic in Pregnant Women using Attribute Mutual Information

2019 ◽  
Vol 9 (2) ◽  
pp. 4959-4961
Keyword(s):  
Health Care ◽  
Mutual Information ◽  
Pregnant Women ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Daily Basis ◽  
Medical Test ◽  
Data Set ◽  
Word Level ◽  
Sentence Level

Since a decade research over sentiment analysis and opinion mining was evolving slowing and emerging widely with greater perspectives and objectives. Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated on a daily basis. This analysis relies on the opinions made by the individuals. These opinions are text, may be positive or negative or a phrase which gives significance to the context. Also these opinions have the power of expressing the context besides drags the attention of new folks. Expressing such opinions ranges from documents level, to the sentence level, to phrase level, to word level and to special symbol level. All these opinion types are labelled with common name Sentiment Analysis. Sentiment Analysis is health care is evolving narrowly with wider research strings. This paper mainly focuses in identifying Sentiments in health care. These sentiments can be medical test values which may be numeric and nominal; sometimes in text too. Such sentiments are identified with pre-fragmentation of data set and Pointwise Mutual Information measure. To accomplish this data of hypertensive pregnant women is considered.

scholarly journals Nystagmus Estimation for Dizziness Diagnosis by Pupil Detection and Tracking Using Mexican-Hat-Type Ellipse Pattern Matching

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 885
Author(s):  
Yoanda Alim Syahbana ◽  
Yokota Yasunari ◽  
Morita Hiroyuki ◽  
Aoki Mitsuhiro ◽  
Suzuki Kanade ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Hough Transform ◽  
University Hospital ◽  
Transform Method ◽  
Data Set ◽  
Medical Specialists ◽  
Detection And Tracking ◽  
Mexican Hat ◽  
Pupil Detection ◽  
In The Wild

The detection of nystagmus using video oculography experiences accuracy problems when patients who complain of dizziness have difficulty in fully opening their eyes. Pupil detection and tracking in this condition affect the accuracy of the nystagmus waveform. In this research, we design a pupil detection method using a pattern matching approach that approximates the pupil using a Mexican hat-type ellipse pattern, in order to deal with the aforementioned problem. We evaluate the performance of the proposed method, in comparison with that of a conventional Hough transform method, for eye movement videos retrieved from Gifu University Hospital. The performance results show that the proposed method can detect and track the pupil position, even when only 20% of the pupil is visible. In comparison, the conventional Hough transform only indicates good performance when 90% of the pupil is visible. We also evaluate the proposed method using the Labelled Pupil in the Wild (LPW) data set. The results show that the proposed method has an accuracy of 1.47, as evaluated using the Mean Square Error (MSE), which is much lower than that of the conventional Hough transform method, with an MSE of 9.53. We conduct expert validation by consulting three medical specialists regarding the nystagmus waveform. The medical specialists agreed that the waveform can be evaluated clinically, without contradicting their diagnoses.

Deaf and Hard-of-hearing Users Evaluating Designs for Highlighting Key Words in Educational Lecture Videos

2021 ◽  
Vol 14 (4) ◽  
pp. 1-24
Author(s):  
Sushant Kafle ◽  
Becca Dingman ◽  
Matt Huenerfauth
Keyword(s):  
Experimental Study ◽  
Key Words ◽  
Hard Of Hearing ◽  
Experimental Studies ◽  
Design Parameters ◽  
Educational Video ◽  
Word Level ◽  
Sentence Level ◽  
First Occurrence ◽  
Educational Videos

There are style guidelines for authors who highlight important words in static text, e.g., bolded words in student textbooks, yet little research has investigated highlighting in dynamic texts, e.g., captions during educational videos for Deaf or Hard of Hearing (DHH) users. In our experimental study, DHH participants subjectively compared design parameters for caption highlighting, including: decoration (underlining vs. italicizing vs. boldfacing), granularity (sentence level vs. word level), and whether to highlight only the first occurrence of a repeating keyword. In partial contrast to recommendations in prior research, which had not been based on experimental studies with DHH users, we found that DHH participants preferred boldface, word-level highlighting in captions. Our empirical results provide guidance for the design of keyword highlighting during captioned videos for DHH users, especially in educational video genres.

scholarly journals Understanding Syntactic and Semantic Errors in the Composition Writing of Jordanian EFL Learners

2017 ◽  
Vol 6 (6) ◽  
pp. 158
Author(s):  
Yazan Shaker Almahameed ◽  
May Al-Shaikhli
Keyword(s):  
Foreign Language ◽  
Language Learners ◽  
Semantic Knowledge ◽  
Resumptive Pronouns ◽  
Foreign Language Learners ◽  
Word Level ◽  
Sentence Level ◽  
Native Speakers Of English ◽  
Semantic Errors ◽  
Verb Tense

The current study aimed at investigating the salient syntactic and semantic errors made by Jordanian English foreign language learners as writing in English. Writing poses a great challenge for both native and non-native speakers of English, since writing involves employing most language sub-systems such as grammar, vocabulary, spelling and punctuation. A total of 30 Jordanian English foreign language learners participated in the study. The participants were instructed to write a composition of no more than one hundred and fifty words on a selected topic. Essays were collected and analyzed statistically to obtain the needed results. The results of the study displayed that syntactic errors produced by the participants were varied, in that eleven types of syntactic errors were committed as follows; verb-tense, agreement, auxiliary, conjunctions, word order, resumptive pronouns, null-subject, double-subject, superlative, comparative and possessive pronouns. Amongst syntactic errors, verb tense errors were the most frequent with 33%. The results additionally revealed that two types of semantic errors were made; errors at sentence level and errors at word level. Errors at word level outstripped by far errors at sentence level, scoring respectively 82% and 18%. It can be concluded that the syntactic and semantic knowledge of Jordanian learners of English is still insufficient.

scholarly journals Product reputation mining: bring informative review summaries to producers and consumers

2019 ◽  
Vol 16 (2) ◽  
pp. 359-380
Author(s):  
Zhehua Piao ◽  
Sang-Min Park ◽  
Byung-Won On ◽  
Gyu Choi ◽  
Myong-Soon Park
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Word Level ◽  
Sentence Level ◽  
Survey Results ◽  
Statistical Studies ◽  
Sentiment Orientation ◽  
Level Method ◽  
Points Of View ◽  
Graph Based Model

Product reputation mining systems can help customers make their buying decision about a product of interest. In addition, it will be helpful to investigate the preferences of recently released products made by enterprises. Unlike the conventional manual survey, it will give us quick survey results on a low cost budget. In this article, we propose a novel product reputation mining approach based on three dimensional points of view that are word, sentence, and aspect?levels. Given a target product, the aspect?level method assigns the sentences of a review document to the desired aspects. The sentence?level method is a graph-based model for quantifying the importance of sentences. The word?level method computes both importance and sentiment orientation of words. Aggregating these scores, the proposed approach measures the reputation tendency and preferred intensity and selects top-k informative review documents about the product. To validate the proposed method, we experimented with review documents relevant with K5 in Kia motors. Our experimental results show that our method is more helpful than the existing lexicon?based approach in the empirical and statistical studies.

scholarly journals INTEGRATED SHORELINE EXTRACTION APPROACH WITH USE OF RASAT MS AND SENTINEL-1A SAR IMAGES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 445-449 ◽  
Author(s):  
N. Demir ◽  
S. Oy ◽  
F. Erdem ◽  
D. Z. Şeker ◽  
B. Bayram
Keyword(s):  
Water Body ◽  
Accuracy Assessment ◽  
Extraction Methods ◽  
Training Data ◽  
Sar Images ◽  
Data Set ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Body Segmentation ◽  
Land Water

Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of Antalya-Kemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.

The synergy of double attention: Combine sentence-level and word-level attention for image captioning

2020 ◽  
Vol 201 ◽  
pp. 103068
Author(s):  
Haiyang Wei ◽  
Zhixin Li ◽  
Canlong Zhang ◽  
Huifang Ma
Keyword(s):  
Image Captioning ◽  
Word Level ◽  
Sentence Level

scholarly journals Pushing the Limits of Translation Quality Estimation

2017 ◽  
Vol 5 ◽  
pp. 205-218 ◽  
Author(s):  
André F. T. Martins ◽  
Marcin Junczys-Dowmunt ◽  
Fabio N. Kepler ◽  
Ramón Astudillo ◽  
Chris Hokamp ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Neural Model ◽  
Quality Estimation ◽  
Translation Quality ◽  
Word Level ◽  
Current State ◽  
Sentence Level ◽  
Feature Based ◽  
Human Effort ◽  
Estimation System

Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways. However, this potential is currently limited by the relatively low accuracy of existing systems. In this paper, we achieve remarkable improvements by exploiting synergies between the related tasks of word-level quality estimation and automatic post-editing. First, we stack a new, carefully engineered, neural model into a rich feature-based word-level quality estimation system. Then, we use the output of an automatic post-editing system as an extra feature, obtaining striking results on WMT16: a word-level FMULT1 score of 57.47% (an absolute gain of +7.95% over the current state of the art), and a Pearson correlation score of 65.56% for sentence-level HTER prediction (an absolute gain of +13.36%).

Fast Convolutions of Packed Strings and Pattern Matching with Wildcards

2017 ◽  
Vol 28 (03) ◽  
pp. 289-307
Author(s):  
Meng Zhang
Keyword(s):  
Pattern Matching ◽  
Time Algorithm ◽  
Matching Algorithm ◽  
Word Level ◽  
Fast Convolutions ◽  
Level Parallelism

We give faster methods to compute discrete convolutions. We assume that all the inputs are packed, that is, strings are packed into words such that each word is packed with [Formula: see text] characters, where w is the length of a machine word and ∑ is the alphabet. The output of our methods is also packed, that is, each word of the output contains more than one element of the result. The approach is based on the word-level parallelism and the FFT. Given two strings with m and n ( n ≥ m ) characters that are packed into [Formula: see text] and [Formula: see text] words respectively, the convolution of them can be computed in [Formula: see text] time, where [Formula: see text] by the FFT. Experiments show that our method is three times faster than the convolution using the standard trick. We consider the problem of pattern matching with wildcards on packed strings. It finds all the occurrences of a pattern in a text, both of which may contain wildcards. By the convolution of packed strings, we present algorithms that are faster than the previous [Formula: see text]-time algorithm, where m is the length of the pattern and n the length of the text. The algorithm runs in [Formula: see text] time, where occ is the number of occurrences of the pattern in the input. Experiments show that the method is faster than the bit-parallel wildcard matching algorithm for long patterns.

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