scholarly journals A Large-scale Text Analysis with Word Embeddings and Topic Modeling

2019 ◽  
Vol 20 (1) ◽  
pp. 147-188
Author(s):  
Won-Joon Choi ◽  
Euhee Kim
Keyword(s):  
Text Analysis ◽  
Topic Modeling ◽  
Large Scale ◽  
Word Embeddings

scholarly journals Lexicons on Demand: Neural Word Embeddings for Large-Scale Text Analysis

2017 ◽  
Author(s):  
Ethan Fast ◽  
Binbin Chen ◽  
Michael S. Bernstein
Keyword(s):  
Text Analysis ◽  
Large Scale ◽  
Data Driven ◽  
Word Embeddings ◽  
Human Language ◽  
Lexical Categories ◽  
On Demand ◽  
Small Set ◽  
Highly Correlated ◽  
The Web

Human language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by learning a neural embedding across billions of words on the web. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated such as neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC.

Investigating Diseases and Chemicals in COVID-19 Literature with Text Mining (Preprint)

2020 ◽  
Author(s):  
Amir Karami ◽  
Brandon Bookstaver ◽  
Melissa Nolan
Keyword(s):  
Text Mining ◽  
Literature Review ◽  
Topic Modeling ◽  
Large Scale ◽  
Clinical Manifestations ◽  
International Health ◽  
Research Papers ◽  
Strategic Plans ◽  
Funding Agencies ◽  
The Relationship

BACKGROUND The COVID-19 pandemic has impacted nearly all aspects of life and has posed significant threats to international health and the economy. Given the rapidly unfolding nature of the current pandemic, there is an urgent need to streamline literature synthesis of the growing scientific research to elucidate targeted solutions. While traditional systematic literature review studies provide valuable insights, these studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, incapable of trend analysis, and lack of data-driven tools. OBJECTIVE This study fills the mentioned restrictions in the literature and practice by analyzing two biomedical concepts, clinical manifestations of disease and therapeutic chemical compounds, with text mining methods in a corpus containing COVID-19 research papers and find associations between the two biomedical concepts. METHODS This research has collected papers representing COVID-19 pre-prints and peer-reviewed research published in 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling to find the relationship between the two biomedical concepts. RESULTS We analyzed 9,298 research papers published through May 5, 2020 and found 3,645 disease-related and 2,434 chemical-related articles. The most frequent clinical manifestations of disease terminology included COVID-19, SARS, cancer, pneumonia, fever, and cough. The most frequent chemical-related terminology included Lopinavir, Ritonavir, Oxygen, Chloroquine, Remdesivir, and water. Topic modeling provided 25 categories showing relationships between our two overarching categories. These categories represent statistically significant associations between multiple aspects of each category, some connections of which were novel and not previously identified by the scientific community. CONCLUSIONS Appreciation of this context is vital due to the lack of a systematic large-scale literature review survey and the importance of fast literature review during the current COVID-19 pandemic for developing treatments. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, to journals for exploring most discussed disease symptoms and pharmaceutical targets, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

Contextual Word Embeddings and Topic Modeling in Healthy Dieting and Obesity

2019 ◽  
Vol 3 (2) ◽  
pp. 159-183 ◽  
Author(s):  
Vijaya Kumari Yeruva ◽  
Sidrah Junaid ◽  
Yugyung Lee
Keyword(s):  
Topic Modeling ◽  
Word Embeddings

Incorporating word embeddings into topic modeling of short text

2018 ◽  
Vol 61 (2) ◽  
pp. 1123-1145 ◽  
Author(s):  
Wang Gao ◽  
Min Peng ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Qianqian Xie ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Word Embeddings ◽  
Short Text

scholarly journals Text Analysis and Visualization Research on the Hetu Dangse During the Qing Dynasty of China

2021 ◽  
Vol 40 (3) ◽  
Author(s):  
Zhiyu Wang ◽  
Jingyu Wu ◽  
Guang Yu ◽  
Zhiping Song
Keyword(s):  
Data Analysis ◽  
Qing Dynasty ◽  
Text Analysis ◽  
Large Scale ◽  
Liaoning Province ◽  
Support Vector ◽  
Historical Documents ◽  
The Qing Dynasty ◽  
Historical Materials ◽  
The Government

In traditional historical research, interpreting historical documents subjectively and manually causes problems such as one-sided understanding, selective analysis, and one-way knowledge connection. In this study, we aim to use machine learning to automatically analyze and explore historical documents from a text analysis and visualization perspective. This technology solves the problem of large-scale historical data analysis that is difficult for humans to read and intuitively understand. In this study, we use the historical documents of the Qing Dynasty Hetu Dangse,preserved in the Archives of Liaoning Province, as data analysis samples. China’s Hetu Dangse is the largest Qing Dynasty thematic archive with Manchu and Chinese characters in the world. Through word frequency analysis, correlation analysis, co-word clustering, word2vec model, and SVM (Support Vector Machines) algorithms, we visualize historical documents, reveal the relationships between functions of the government departments in the Shengjing area of the Qing Dynasty, achieve the automatic classification of historical archives, improve the efficient use of historical materials as well as build connections between historical knowledge. Through this, archivists can be guided practically in historical materials’ management and compilation.

scholarly journals Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity

2020 ◽  
pp. 1-51
Author(s):  
Ivan Vulić ◽  
Simon Baker ◽  
Edoardo Maria Ponti ◽  
Ulla Petti ◽  
Ira Leviant ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Large Scale ◽  
Representation Learning ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lexical Representations ◽  
Language Data ◽  
Weakly Supervised ◽  
Cross Lingual

We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering data sets for 12 typologically diverse languages, including major languages (e.g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e.g., Welsh, Kiswahili). Each language data set is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs, providing a representative coverage of word classes (nouns, verbs, adjectives, adverbs), frequency ranks, similarity intervals, lexical fields, and concreteness levels. Additionally, owing to the alignment of concepts across languages, we provide a suite of 66 crosslingual semantic similarity data sets. Because of its extensive size and language coverage, Multi-SimLex provides entirely novel opportunities for experimental evaluation and analysis. On its monolingual and crosslingual benchmarks, we evaluate and analyze a wide array of recent state-of-the-art monolingual and crosslingual representation models, including static and contextualized word embeddings (such as fastText, monolingual and multilingual BERT, XLM), externally informed lexical representations, as well as fully unsupervised and (weakly) supervised crosslingual word embeddings. We also present a step-by-step data set creation protocol for creating consistent, Multi-Simlex -style resources for additional languages.We make these contributions—the public release of Multi-SimLex data sets, their creation protocol, strong baseline results, and in-depth analyses which can be be helpful in guiding future developments in multilingual lexical semantics and representation learning—available via aWeb site that will encourage community effort in further expansion of Multi-Simlex to many more languages. Such a large-scale semantic resource could inspire significant further advances in NLP across languages.

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