This research explores the use of Natural Language Processing (NLP) techniques to locate geological resources, with a specific focus on industrial minerals. By using word embeddings trained with the GloVe model, we extract semantic relationships between target keywords and a corpus of geological texts. The text is filtered to retain only words with geographical significance, such as city names, which are then ranked by their cosine similarity to the target keyword. Dimensional reduction techniques, including Principal Component Analysis (PCA), Autoencoder, Variational Autoencoder (VAE), and VAE with Long Short-Term Memory (VAE-LSTM), are applied to enhance feature extraction and improve the accuracy of semantic relations. For benchmarking, we calculate the proximity between the ten cities most semantically related to the target keyword and identified mine locations using the haversine equation. The results demonstrate that combining NLP with dimensional reduction techniques provides meaningful insights into the spatial distribution of natural resources. Although the result shows to be in the same region as the supposed location, the accuracy has room for improvement.

Embedding news articles is a crucial tool for multiple fields, such as media bias detection, identifying fake news, and news recommendations. However, existing news embedding methods are not optimized for capturing the latent context of news events. In many cases, news embedding methods rely on full-textual information and neglect the importance of time-relevant embedding generation. Here, we aim to address these shortcomings by presenting a novel lightweight method that optimizes news embedding generation by focusing on the entities and themes mentioned in the articles and their historical connections to specific events. We suggest a method composed of three stages. First, we process and extract the events, entities, and themes for the given news articles. Second, we generate periodic time embeddings for themes and entities by training timely separated GloVe models on current and historical data. Lastly, we concatenate the news embeddings generated by two distinct approaches: Smooth Inverse Frequency (SIF) for article-level vectors and Siamese Neural Networks for embeddings with nuanced event-related information. To test and evaluate our method, we leveraged over 850,000 news articles and 1,000,000 events from the GDELT project. For validation purposes, we conducted a comparative analysis of different news embedding generation methods, applying them twice to a shared event detection task - first on articles published within the same day and subsequently on those published within the same month. Our experiments show that our method significantly improves the Precision-Recall (PR) AUC across all tasks and datasets. Specifically, we observed an average PR AUC improvement of 2.15% and 2.57% compared to SIF, as well as 2.57% and 2.43% compared to the semi-supervised approach for daily and monthly shared event detection tasks, respectively.

This study addresses the challenges of multi-label text classification. The difficulties arise from imbalanced data sets, varied text lengths, and numerous subjective feature labels. Existing solutions include traditional machine learning and deep neural networks for predictions. However, both approaches have their limitations. Traditional machine learning often overlooks the associations between words, while deep neural networks, despite their better classification performance, come with increased training complexity and time. This paper proposes a method utilizing the bag-of-words model approach based on the GloVe model and the CNN-BiLSTM network. The principle is to use the word vector matrix trained by the GloVe model as the input for the text embedding layer. Given that the GloVe model requires no further training, the neural network model can be trained more efficiently. The method achieves an accuracy rate of 87.26% on the test set and an F1 score of 0.8737, showcasing promising results.

Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in different domains, they are limited in addressing fine-grained analysis on educational and multilingual corpora. In this work, we analyze bias across text and through multiple architectures on a corpus of 9,165 German peer-reviews collected from university students over five years. Notably, our corpus includes labels such as helpfulness, quality, and critical aspect ratings from the peer-review recipient as well as demographic attributes. We conduct a Word Embedding Association Test (WEAT) analysis on (1) our collected corpus in connection with the clustered labels, (2) the most common pre-trained German language models (T5, BERT, and GPT-2) and GloVe embeddings, and (3) the language models after fine-tuning on our collected data-set. In contrast to our initial expectations, we found that our collected corpus does not reveal many biases in the co-occurrence analysis or in the GloVe embeddings. However, the pre-trained German language models find substantial conceptual, racial, and gender bias and have significant changes in bias across conceptual and racial axes during fine-tuning on the peer-review data. With our research, we aim to contribute to the fourth UN sustainability goal (quality education) with a novel dataset, an understanding of biases in natural language education data, and the potential harms of not counteracting biases in language models for educational tasks.

In the previous post, we introduced NLP. To find out word meanings with the Python programming language, we used the NLTK package and worked our way into word embeddings using the gensim package and Word2vec. Since we only touched the Word2Vec technique from a 10,000-feet overview, we are now going to dive deeper into the training method to create a Word2vec model. The Word2vec (Mikolov et al. 2013)[1][2] is not a singular technique or algorithm. It's actually a family of neural network architectures and optimization techniques that can produce good results learning embeddings for large datasets.

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec. However the technique is different and training is performed on an aggregated global word-word co-occurrence matrix, giving us a vector space with meaningful sub-structures. This method was invented in Stanford by Pennington et al. and I recommend you to read the original paper on GloVe, 'GloVe: Global Vectors for Word Representation' by Pennington et al. which is an excellent read to get some perspective on how this model works. We won't cover the implementation of the model from scratch in too much detail here but if you are interested in the actual code, you can check out the official GloVe page.

Quora recently announced the first public dataset that they ever released. It includes 404351 question pairs with a label column indicating if they are duplicate or not. In this post, I like to investigate this dataset and at least propose a baseline method with deep learning. Beside the proposed method, it includes some examples showing how to use Pandas, Gensim, Spacy and Keras. For the full code you check Github.

The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al. is reported to be an efficient and effective method for learning vector representations of words. State-of-the-art performance is also provided by skip-gram with negative-sampling (SGNS) implemented in the word2vec tool. In this note, we explain the similarities between the training objectives of the two models, and show that the objective of SGNS is similar to the objective of a specialized form of GloVe, though their cost functions are defined differently.