scholarly journals AMATCHMETHOD BASED ON LATENT SEMANTIC ANALYSIS FOR EARTHQUAKEHAZARD EMERGENCY PLAN

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 137-141
Author(s):  
D. Sun ◽  
S. Zhao ◽  
Z. Zhang ◽  
X. Shi
Keyword(s):  
Decision Maker ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Semantic Space ◽  
Word Segmentation ◽  
Emergency Plan ◽  
Match Method ◽  
Part Of Speech ◽  
Plan Retrieval ◽  
Short Time

The structure of the emergency plan on earthquake is complex, and it’s difficult for decision maker to make a decision in a short time. To solve the problem, this paper presents a match method based on Latent Semantic Analysis (LSA). After the word segmentation preprocessing of emergency plan, we carry out keywords extraction according to the part-of-speech and the frequency of words. Then through LSA, we map the documents and query information to the semantic space, and calculate the correlation of documents and queries by the relation between vectors. The experiments results indicate that the LSA can improve the accuracy of emergency plan retrieval efficiently.

Intelligent Match of Emergency Plan Based on Chinese Keywords Extraction

2012 ◽  
Vol 263-266 ◽  
pp. 1652-1658
Author(s):  
Huai Guang Wu ◽  
Qing Lin ◽  
Zhong Ju Fu
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Emergency Plan ◽  
Linear Estimate ◽  
Match Method ◽  
Space Model ◽  
Part Of Speech ◽  
Estimate Method ◽  
Set Up ◽  
Maximum Similarity

This paper introduced an intelligent match method of emergency plan based on keywords extraction. Words frequency, part of speech and position of framework are taken as the keyword weight factors. Least-squares error linear estimate method is used to regulate the factors and calculate keywords weight. And Vector Space Model is set up to calculate the maximum similarity between plan texts to complete design of plan match. The expansible practical parameters adjustment module is provided to adapt to diversity of match plan with emphasis part. Compare with tf*idf, the experimental results show that the presented method is more promising in intelligent match method of emergency plan.

The Application of LSA in TCM Syndromes Classification

2013 ◽  
Vol 284-287 ◽  
pp. 1666-1670
Author(s):  
Yan Fang Gan ◽  
Zi Wei Ni ◽  
Fan Lin
Keyword(s):  
Health Care ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Manifestation ◽  
Health Care Workers ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Semantic Space ◽  
Primary Objective ◽  
Care Workers

The description of syndromes and symptoms in traditional Chinese medicine (TCM) is extremely complicated. And how to diagnose the patient's syndrome in a better way is the primary objective of clinical health care workers all the time. It was a good attempt to diagnose patient's syndrome by combining Latent Semantic Analysis and the feature of TCM knowledge----both syndromes and organs have the same clinical manifestation collection that are symptoms. In this paper, correlative degrees would be computed and sorted in a certain latent semantic space which was constructed by syndromes and organs . According to the result of correlative degrees computing, the classifying could be done by choosing the highest correlative degree as the belonging class. The experimental results show that this method performs quite well.

scholarly journals Extracting Moods from Songs and BBC Programs Based on Emotional Context

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Michael Kai Petersen ◽  
Andrius Butkus
Keyword(s):  
Social Network ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Semantic Space ◽  
User Preferences ◽  
Intelligent Interfaces ◽  
Emotional Context ◽  
Early Results ◽  
Digital Broadcast ◽  
Selection Of

The increasing amounts of media becoming available in converged digital broadcast and mobile broadband networks will require intelligent interfaces capable of personalizing the selection of content. Aiming to capture the mood in the content, we construct a semantic space based on tags, frequently used to describe emotions associated with music in thelast.fmsocial network. Implementing latent semantic analysis (LSA), we model the affective context of songs based on their lyrics, and apply a similar approach to extract moods from BBC synopsis descriptions of TV episodes using TV-Anytime atmosphere terms. Based on our early results, we propose that LSA could be implemented as machinelearning method to extract emotional context and model affective user preferences.

scholarly journals A case study of applying text analysis to identify possible adverse drug interactions: The case of Adalat (Nifedipine)

2019 ◽  
Vol 26 (2) ◽  
pp. 1455-1464
Author(s):  
David Gefen ◽  
Ofir Ben-Assuli ◽  
Nir Shlomo ◽  
Noreen Robertson ◽  
Robert Klempfner
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Electronic Health Records ◽  
Drug Interactions ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Medical Center ◽  
Semantic Space ◽  
Health Records ◽  
Electronic Health

Adalat (Nifedipine) is a calcium-channel blocker that is also used as an antihypertensive drug. The drug was approved by the US Food and Drug Administration in 1985 but was discontinued in 1996 on account, among other things, of interactions with other medications. Nonetheless, Adalat is still used in other countries to treat congestive heart failure. We examine all the congestive heart failure electronic health records of the largest medical center in Israel to discover whether, possibly, taking Adalat with other medications is associated with patient death. This study examines a semantic space built by running latent semantic analysis on the entire corpus of congestive heart failure electronic health records of that medical center, encompassing 8 years of data on almost 12,000 patients. Through this semantic space, the most highly correlated medications and medical conditions that co-occurred with Adalat were identified. This was done separately for men and women. The results show that Adalat is correlated with different medications and conditions across genders. The data also suggest that taking Adalat with Captopril (angiotensin-converting enzyme inhibitor) or Rulid (antibiotic) might be dangerous in both genders. The study thus demonstrates the potential of applying latent semantic analysis to identify potentially dangerous drug interactions that may have otherwise gone under the radar.

scholarly journals A studyforrest extension, an annotation of spoken language in the German dubbed movie “Forrest Gump” and its audio-description

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 54
Author(s):  
Christian Olaf Häusler ◽  
Michael Hanke
Keyword(s):  
Semantic Analysis ◽  
Brain Activity ◽  
Real Life ◽  
Semantic Space ◽  
Functional Brain Imaging ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Grammatical Categories ◽  
Audio Description ◽  
Speech Tagging

Here we present an annotation of speech in the audio-visual movie “Forrest Gump” and its audio-description for a visually impaired audience, as an addition to a large public functional brain imaging dataset (studyforrest.org). The annotation provides information about the exact timing of each of the more than 2500 spoken sentences, 16,000 words (including 202 non-speech vocalizations), 66,000 phonemes, and their corresponding speaker. Additionally, for every word, we provide lemmatization, a simple part-of-speech-tagging (15 grammatical categories), a detailed part-of-speech tagging (43 grammatical categories), syntactic dependencies, and a semantic analysis based on word embedding which represents each word in a 300-dimensional semantic space. To validate the dataset’s quality, we build a model of hemodynamic brain activity based on information drawn from the annotation. Results suggest that the annotation’s content and quality enable independent researchers to create models of brain activity correlating with a variety of linguistic aspects under conditions of near-real-life complexity.

Learning and Representing Verbal Meaning

1998 ◽  
Vol 7 (5) ◽  
pp. 161-164 ◽  
Author(s):  
Thomas K. Landauer
Keyword(s):  
Subject Matter ◽  
Latent Semantic Analysis ◽  
Representative Sample ◽  
Semantic Analysis ◽  
Word Meaning ◽  
Semantic Space ◽  
Human Judgment ◽  
Conceptual Content ◽  
And Behavior ◽  
Verbal Meaning

Latent semantic analysis (LSA) is a theory of how word meaning—and possibly other knowledge—is derived from statistics of experience, and of how passage meaning is represented by combinations of words. Given a large and representative sample of text, LSA combines the way thousands of words are used in thousands of contexts to map a point for each into a common semantic space. LSA goes beyond pair-wise co-occurrence or correlation to find latent dimensions of meaning that best relate every word and passage to every other. After learning from comparable bodies of text, LSA has scored almost as well as humans on vocabulary and subject-matter tests, accurately simulated many aspects of human judgment and behavior based on verbal meaning, and been successfully applied to measure the coherence and conceptual content of text. The surprising success of LSA has implications for the nature of generalization and language.

scholarly journals “Evaluator”

2014 ◽  
pp. 1601-1626
Author(s):  
Paz Ferrero ◽  
Rachel Whittaker ◽  
Javier Alda
Keyword(s):  
Computational Linguistics ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Native Speakers ◽  
Syntactic Analysis ◽  
Quantitative Indices ◽  
Part Of Speech ◽  
Semantic Fields ◽  
The Common ◽  
The One

Computational linguistics can offer tools for automatic grading of written texts. “Evaluator” is such a tool. It uses FreeLing as a morpho-syntactic analyzer, providing words, lemmas, and part of speech tags for each word in a text. Multi-words can also be identified and their grammar identified. “Evaluator” also manages leveled glossaries, like the one developed by the Instituto Cervantes, as well as other electronically available dictionaries. All these glossaries enable the tool to identify most words in texts, grading them into the six levels scale of the Common European Framework of Reference for Languages. To assign a lexical level to the text under analysis, a statistical distribution of leveled qualified lemmas is used. Other ways to assign a lexical level to a text by using corpora of a preset level are also suggested. The syntactic analysis is based on a collection of grammar structures leveled by following the descriptors given by the Instituto Cervantes. These grammar structures are identified within the text using quantitative indices which level a text by comparing it with a given corpus. Finally, semantic identification is done using semantic fields as defined by the Instituto Cervantes. Latent Semantic Analysis is also used to group texts dealing with the same topic together. All these methods have been tested and applied to real texts written in Spanish by native speakers and learners.

“Evaluator”

2013 ◽  
pp. 244-269
Author(s):  
Paz Ferrero ◽  
Rachel Whittaker ◽  
Javier Alda
Keyword(s):  
Computational Linguistics ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Native Speakers ◽  
Syntactic Analysis ◽  
Quantitative Indices ◽  
Part Of Speech ◽  
Semantic Fields ◽  
The Common ◽  
The One

Computational linguistics can offer tools for automatic grading of written texts. “Evaluator” is such a tool. It uses FreeLing as a morpho-syntactic analyzer, providing words, lemmas, and part of speech tags for each word in a text. Multi-words can also be identified and their grammar identified. “Evaluator” also manages leveled glossaries, like the one developed by the Instituto Cervantes, as well as other electronically available dictionaries. All these glossaries enable the tool to identify most words in texts, grading them into the six levels scale of the Common European Framework of Reference for Languages. To assign a lexical level to the text under analysis, a statistical distribution of leveled qualified lemmas is used. Other ways to assign a lexical level to a text by using corpora of a preset level are also suggested. The syntactic analysis is based on a collection of grammar structures leveled by following the descriptors given by the Instituto Cervantes. These grammar structures are identified within the text using quantitative indices which level a text by comparing it with a given corpus. Finally, semantic identification is done using semantic fields as defined by the Instituto Cervantes. Latent Semantic Analysis is also used to group texts dealing with the same topic together. All these methods have been tested and applied to real texts written in Spanish by native speakers and learners.

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