Combining Word Embeddings and Deep Neural Networks for Job Offers and Resumes Classification in IT Recruitment Domain

Interventional cancer clinical trials are generally too restrictive, and some patients are often excluded on the basis of comorbidity, past or concomitant treatments, or the fact that they are over a certain age. The efficacy and safety of new treatments for patients with these characteristics are, therefore, not defined. In this work, we built a model to automatically predict whether short clinical statements were considered inclusion or exclusion criteria. We used protocols from cancer clinical trials that were available in public registries from the last 18 years to train word-embeddings, and we constructed a dataset of 6M short free-texts labeled as eligible or not eligible. A text classifier was trained using deep neural networks, with pre-trained word-embeddings as inputs, to predict whether or not short free-text statements describing clinical information were considered eligible. We additionally analyzed the semantic reasoning of the word-embedding representations obtained and were able to identify equivalent treatments for a type of tumor analogous with the drugs used to treat other tumors. We show that representation learning using deep neural networks can be successfully leveraged to extract the medical knowledge from clinical trial protocols for potentially assisting practitioners when prescribing treatments.

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Sentiment Analysis of Short Russian Texts Using BERT and Word2Vec Embeddings

10.20948/graphicon-2021-3027-1011-1016 ◽

2021 ◽

Author(s):

Ekaterina Popova ◽

Vladimir Spitsyn

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Social Networks ◽

Sentiment Analysis ◽

Deep Neural Networks ◽

Functional Method ◽

Fine Tuning ◽

Machine Learning Techniques ◽

Word Embeddings ◽

Learning Techniques

This article is devoted to modern approaches for sentiment analysis of short Russian texts from social networks using deep neural networks. Sentiment analysis is the process of detecting, extracting, and classifying opinions, sentiments, and attitudes concerning different topics expressed in texts. The importance of this topic is linked to the growth and popularity of social networks, online recommendation services, news portals, and blogs, all of which contain a significant number of people's opinions on a variety of topics. In this paper, we propose machine-learning techniques with BERT and Word2Vec embeddings for tweets sentiment analysis. Two approaches were explored: (a) a method, of word embeddings extraction and using the DNN classifier; (b) refinement of the pre-trained BERT model. As a result, the fine- tuning BERT outperformed the functional method to solving the problem.

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Deep Neural Networks for Query Expansion Using Word Embeddings

Lecture Notes in Computer Science - Advances in Information Retrieval ◽

10.1007/978-3-030-15719-7_26 ◽

2019 ◽

pp. 203-210 ◽

Cited By ~ 7

Author(s):

Ayyoob Imani ◽

Amir Vakili ◽

Ali Montazer ◽

Azadeh Shakery

Keyword(s):

Neural Networks ◽

Query Expansion ◽

Deep Neural Networks ◽

Word Embeddings

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Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks

10.18653/v1/n16-1041 ◽

2016 ◽

Cited By ~ 10

Author(s):

Yangtuo Peng ◽

Hui Jiang

Keyword(s):

Neural Networks ◽

Stock Price ◽

Deep Neural Networks ◽

Word Embeddings ◽

Financial News ◽

Price Movements ◽

Stock Price Movements

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Adjusting Word Embeddings by Deep Neural Networks

Proceedings of the 9th International Conference on Agents and Artificial Intelligence ◽

10.5220/0006120003980406 ◽

2017 ◽

Cited By ~ 1

Author(s):

Xiaoyang Gao ◽

Ryutaro Ichise

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Word Embeddings

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CASIE: Extracting Cybersecurity Event Information from Text

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6401 ◽

2020 ◽

Vol 34 (05) ◽

pp. 8749-8757 ◽

Cited By ~ 2

Author(s):

Taneeya Satyapanich ◽

Francis Ferraro ◽

Tim Finin

Keyword(s):

Neural Networks ◽

Event Detection ◽

Deep Neural Networks ◽

Knowledge Graph ◽

Semantic Model ◽

Word Embeddings ◽

Linguistic Features ◽

Semantic Roles ◽

Event Information ◽

Event Based

We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of 1,000 English news articles from 2017–2019 that are labeled with rich, event-based annotations and that covers both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types (e.g., file, device, software, money). CASIE uses different deep neural networks approaches with attention and can incorporate rich linguistic features and word embeddings. We have conducted experiments on each component in the event detection pipeline and the results show that each subsystem performs well.

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