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Found 12 papers, 11 papers with code
Exploring Word Usage Change with Continuously Evolving Embeddings
The usage of individual words can change over time, for example, when words experience a semantic shift.
Forecasting Industrial Aging Processes with Machine Learning Methods
Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant.
The autofeat Python Library for Automated Feature Engineering and Selection
This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities.
Automating the search for a patent's prior art with a full text similarity search
The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality.
The DALPHI annotation framework & how its pre-annotations can improve annotator efficiency
In a study with 66 participants, we demonstrate on the exemplary task of annotating named entities in text documents that with this assistance system the annotation processes can be improved with respect to the quality and quantity of produced annotations, even if the pre-annotations provided by the assistance system are at a recall level of only 50%.
Information Retrieval Human-Computer Interaction H.3.4; H.3.5; D.2.2
Discovering topics in text datasets by visualizing relevant words
When dealing with large collections of documents, it is imperative to quickly get an overview of the texts' contents.
Exploring text datasets by visualizing relevant words
When working with a new dataset, it is important to first explore and familiarize oneself with it, before applying any advanced machine learning algorithms.
Interactive Exploration and Discovery of Scientific Publications with PubVis
With an exponentially growing number of scientific papers published each year, advanced tools for exploring and discovering publications of interest are becoming indispensable.
Context encoders as a simple but powerful extension of word2vec
With a simple architecture and the ability to learn meaningful word embeddings efficiently from texts containing billions of words, word2vec remains one of the most popular neural language models used today.
Predicting Pairwise Relations with Neural Similarity Encoders
Matrix factorization is at the heart of many machine learning algorithms, for example, dimensionality reduction (e. g. kernel PCA) or recommender systems relying on collaborative filtering.
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment.
Explaining Predictions of Non-Linear Classifiers in NLP
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables.
