Lexical Density Analysis of Word Productions in Japanese English Using Acoustic Word Embeddings

2021 ◽  
Author(s):  
Shintaro Ando ◽  
Nobuaki Minematsu ◽  
Daisuke Saito
Keyword(s):  
Word Embeddings ◽  
Density Analysis ◽  
Lexical Density ◽  
Japanese English

Learning Japanese-English Bilingual Word Embeddings by Using Language Specificity

2021 ◽  
pp. 2050014
Author(s):  
Yuting Song ◽  
Biligsaikhan Batjargal ◽  
Akira Maeda
Keyword(s):  
Linear Mapping ◽  
Word Embedding ◽  
Word Embeddings ◽  
Semantic Meaning ◽  
Advanced Method ◽  
Bilingual Lexicon ◽  
English Bilingual ◽  
Specific Mapping ◽  
Cross Lingual ◽  
Japanese English

Cross-lingual word embeddings have been gaining attention because they can capture the semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform a word embedding space from one language to another. To improve bilingual word embeddings, we propose an advanced method that adds a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of the Japanese language. We evaluated our method by comparing it with single mapping-based-models on bilingual lexicon induction between Japanese and English. We determined that our method was more effective, with significant improvements on words of Japanese origin.

scholarly journals Pre-Reordering for Neural Machine Translation: Helpful or Harmful?

2017 ◽  
Vol 108 (1) ◽  
pp. 171-182 ◽  
Author(s):  
Jinhua Du ◽  
Andy Way
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Word Class ◽  
Word Embeddings ◽  
Neural Machine Translation ◽  
Parts Of Speech ◽  
Translation Quality ◽  
The Impact ◽  
Japanese English ◽  
Target Side

AbstractPre-reordering, a preprocessing to make the source-side word orders close to those of the target side, has been proven very helpful for statistical machine translation (SMT) in improving translation quality. However, is it the case in neural machine translation (NMT)? In this paper, we firstly investigate the impact of pre-reordered source-side data on NMT, and then propose to incorporate features for the pre-reordering model in SMT as input factors into NMT (factored NMT). The features, namely parts-of-speech (POS), word class and reordered index, are encoded as feature vectors and concatenated to the word embeddings to provide extra knowledge for NMT. Pre-reordering experiments conducted on Japanese↔English and Chinese↔English show that pre-reordering the source-side data for NMT is redundant and NMT models trained on pre-reordered data deteriorate translation performance. However, factored NMT using SMT-based pre-reordering features on Japanese→English and Chinese→English is beneficial and can further improve by 4.48 and 5.89 relative BLEU points, respectively, compared to the baseline NMT system.

LEXICAL DENSITY ANALYSIS OF READING TEXT IN BAHASA INGGRIS TEXTBOOK FOR GRADE XI SMAN 2 TEBING TINGGI

2017 ◽  
Vol 6 (4) ◽  
Author(s):  
Dibasari Putri And Yeni Arlita
Keyword(s):  
High School ◽  
Global Warming ◽  
Orange Juice ◽  
Senior High School ◽  
Density Level ◽  
Experiential Mode ◽  
Reading Text ◽  
Density Analysis ◽  
Lexical Density ◽  
Reading Texts

This study deals with the lexical density of reading text of English textbook for senior high school. It was aimed to find out lexical density level in the reading text,how is the lexical density distributed across the texts and then why is the lexical density used in the textbook. This study was conducted by using qualitative research.The data of study were the 8 texts that classified by 4 genres in the Bahasa Inggris textbook for grade XI SMAN 2 Tebing Tingi. The data were analyzed by using Eggins (2004) theory. The result of this study: 1) The LD level of text was equal based on Eggins theory. 2) The distribution of lexical density in reading text in Bahasa Inggris textbook as follows: Text 1 entitle “ How to Make Orange Juice” was 0.37. Text 2 entitle “ How to plant Jasmine “ was 0.46. Text 3 entitle “ How to Make Cheese Toast” was 0.37. Text 4 entitle “ How to Make a Pizza” was 0.44. Text 5 entitle “ Earthquakes “ was 0.46. Text 6 entitle “ The Last “ was 0.32. Text 7 entitle "Life and Times of Ki Hajar Dewantara” was 0.38. Text 8 entitle“ Global Warming” was 0.47. 3) Although the reading texts in the bahasa inggris textbook had different genre in each texts but it didn’t influence lexical density of the text because the influencing of the high and low lexical density in the texts were the three points of experiential mode namely action, reconstraction and generalization.

scholarly journals A Critical Comparison between Manufacturing and Service Firms’ Mission Statements: Manage Your Firms’ Mission Statements with Care Using Readability and Lexical Techniques

2020 ◽  
Vol 11 (1) ◽  
pp. 216
Author(s):  
Sheikh Shamim Hasnain
Keyword(s):  
Mission Statements ◽  
Manufacturing Firms ◽  
Service Firms ◽  
Flesch Reading Ease ◽  
Result Show ◽  
Ease Score ◽  
Density Analysis ◽  
Data Arrangement ◽  
Lexical Density ◽  
Practical Implications

This paper aims to investigate the Readability and Lexical Density of the mission statements of the large service and manufacturing firms. In respect of mission statements, a comparison between the service and manufacturing firms is drawn. For initial data arrangement, the mission statements of all selected service firms are grouped together, same was also done with the manufacturing firms separately. The mission statements are processed through the software for Readability (Gunning Fog, Flesch-Kincaid, SMOG, Coleman-Liau, Automated, Flesch Reading Ease score) and Lexical Density analysis in two categories, service firms and manufacturing firms. Result show that Service firms’ mission statements are more Lexically dense and possess higher average level than those of the manufacturing firms. This study contributes to the Strategic Management literature and practical implications to the service and manufacturing firms and their stakeholders. Also comes out of the ole ways of data analysis in management studies. Future researchers may carry out similar research in a specific industry.

Spatiotemporal distribution of foraging in a marine predator: behavioural drivers of hotspot formation

2020 ◽  
Vol 635 ◽  
pp. 187-202
Author(s):  
T Brough ◽  
W Rayment ◽  
E Slooten ◽  
S Dawson
Keyword(s):  
Time Of Day ◽  
Spatiotemporal Distribution ◽  
Temporal Effects ◽  
Marine Predator ◽  
Marine Predators ◽  
Additive Mixed Models ◽  
Density Analysis ◽  
The Times ◽  
Passive Acoustic ◽  
Spatial Locations

Many species of marine predators display defined hotspots in their distribution, although the reasons why this happens are not well understood in some species. Understanding whether hotspots are used for certain behaviours provides insights into the importance of these areas for the predators’ ecology and population viability. In this study, we investigated the spatiotemporal distribution of foraging behaviour in Hector’s dolphin Cephalorhynchus hectori, a small, endangered species from New Zealand. Passive acoustic monitoring of foraging ‘buzzes’ was carried out at 4 hotspots and 6 lower-use, ‘reference areas’, chosen randomly based on a previous density analysis of visual sightings. The distribution of buzzes was modelled among spatial locations and on 3 temporal scales (season, time of day, tidal state) with generalised additive mixed models using 82000 h of monitoring data. Foraging rates were significantly influenced by all 3 temporal effects, with substantial variation in the importance and nature of each effect among locations. The complexity of the temporal effects on foraging is likely due to the patchy nature of prey distributions and shows how foraging is highly variable at fine scales. Foraging rates were highest at the hotspots, suggesting that feeding opportunities shape fine-scale distribution in Hector’s dolphin. Foraging can be disrupted by anthropogenic influences. Thus, information from this study can be used to manage threats to this vital behaviour in the locations and at the times where it is most prevalent.

Contact Current Density Analysis Inside Human Body in Low-Frequency Band Using Geometric Multi-Grid Solver

2020 ◽  
Vol E103.C (11) ◽  
pp. 588-596
Author(s):  
Masamune NOMURA ◽  
Yuki NAKAMURA ◽  
Hiroo TARAO ◽  
Amane TAKEI
Keyword(s):  
Frequency Band ◽  
Current Density ◽  
Human Body ◽  
Low Frequency ◽  
Density Analysis
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