- When a word appears in different contexts, its vector gets moved in different directions during updates. The final vector then represents some sort of weighted average over the various contexts. Averaging over vectors that point in different directions typically results in a vector that gets shorter with increasing number of different contexts in which the word appears. For words to be used in many different contexts, they must carry little meaning. Prime examples of such insignificant words are high-frequency stop words, which are indeed represented by short vectors despite their high term frequencies ...
- When the downstream applications only care about the direction of the word vectors (e.g. they only pay attention to the cosine similarity of two words), then normalize, and forget about length. However, if the downstream applications are able to (or need to) consider more sensible aspects, such as word significance, or consistency in word usage (see below), then normalization might not be such a good idea.
- This page is a distribution site for movie-review data for use in sentiment-analysis experiments. Available are collections of movie-review documents labeled with respect to their overall sentiment polarity (positive or negative) or subjective rating (e.g., "two and a half stars") and sentences labeled with respect to their subjectivity status (subjective or objective) or polarity. These data sets were introduced in the following papers:
- MACE (Multi-Annotator Competence Estimation) is an implementation of an item-response model that let's you evaluate redundant annotations of categorical data. It provides competence estimates of the individual annotators and the most likely answer to each item. If we have 10 annotators answer a question, and five answer with 'yes' and five with 'no' (a surprisingly frequent event), we would normaly have to flip a coin to decide what the right answer is. If we knew, however, that one of the people who answered 'yes' is an expert on the question, while one of the others just alwas selects 'no', we would take this information into account to weight their answers. MACE does exactly that. It tries to find out which annotators are more trustworthy and upweighs their answers. All you need to provide is a CSV file with one item per line. In tests, MACE's trust estimates correlated highly wth the annotators' true competence, and it achieved accuracies of over 0.9 on several test sets. MACE can take annotated items into account, if they are available. This helps to guide the training and improves accuracy.
- Identifying meaningful topics for sparse Steam reviews -- by Steve Shao
- Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data The data is uniformly distributed on Riemannian manifold; The Riemannian metric is locally constant (or can be approximated as such); The manifold is locally connected. From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.
- D-Tale is an interactive web-based library that consists of a Flask backend and a React front-end serving as an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. Currently, this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex.
- You want to discern how many clusters we have (or, if you prefer, how many gaussians components generated the data), and you don’t have information about the “ground truth”. A real case, where data do not have the nicety of behaving good as the simulated ones.
- Definition of NLP coherence scores, in particular intrinsic UMass measure and PMI. Human judgment not being correlated to perplexity (or likelihood of unseen documents) is the motivation for more work trying to model the human judgment. This is by itself a hard task as human judgment is not clearly defined; for example, two experts can disagree on the usefulness of a topic. One can classify the methods addressing this problem into two categories. \textit{Intrinsic} methods that do not use any external source or task from the dataset, whereas \textit{extrinsic} methods use the discovered topics for external tasks, such as information retrieval [Wei06], or use external statistics to evaluate topics.
- The main aim of SenticNet is to make the conceptual and affective information conveyed by natural language (meant for human consumption) more easily-accessible to machines.
- In this article, we’ll look at Weakly Supervised Learning (WSL), which provides a solution by leveraging “weak” annotations to learn the task. But before we dive deeper into the techniques, it is worth exploring the various types of WSL techniques and the sections we intend to cover in this article.
- In recent years, the real-world impact of machine learning (ML) has grown in leaps and bounds. In large part, this is due to the advent of deep learning models, which allow practitioners to get state-of-the-art scores on benchmark datasets without any hand-engineered features. Given the availability of multiple open-source ML frameworks like TensorFlow and PyTorch, and an abundance of available state-of-the-art models, it can be argued that high-quality ML models are almost a commoditized resource now. There is a hidden catch, however: the reliance of these models on massive sets of hand-labeled training data. These hand-labeled training sets are expensive and time-consuming to create — often requiring person-months or years to assemble, clean, and debug — especially when domain expertise is required. On top of this, tasks often change and evolve in the real world. For example, labeling guidelines, granularities, or downstream use cases often change, necessitating re-labeling (e.g., instead of classifying reviews only as positive or negative, introducing a neutral category). For all these reasons, practitioners have increasingly been turning to weaker forms of supervision, such as heuristically generating training data with external knowledge bases, patterns/rules, or other classifiers. Essentially, these are all ways of programmatically generating training data—or, more succinctly, programming training data. We begin by reviewing areas of ML that are motivated by the problem of labeling training data, and then describe our research on modeling and integrating a diverse set of supervision sources. We also discuss our vision for building data management systems for the massively multi-task regime with tens or hundreds of weakly supervised dynamic tasks interacting in complex and varied ways. Check out the our research blog for detailed discussions of these topics and more!
- In mathematics, the Wasserstein or Kantorovich–Rubinstein metric or distance is a distance function defined between probability distributions on a given metric space M {\displaystyle M} M. Intuitively, if each distribution is viewed as a unit amount of "dirt" piled on M {\displaystyle M} M, the metric is the minimum "cost" of turning one pile into the other, which is assumed to be the amount of dirt that needs to be moved times the mean distance it has to be moved. Because of this analogy, the metric is known in computer science as the earth mover's distance.
- In statistics, the Bhattacharyya distance measures the similarity of two probability distributions. It is closely related to the Bhattacharyya coefficient which is a measure of the amount of overlap between two statistical samples or populations. Both measures are named after Anil Kumar Bhattacharya, a statistician who worked in the 1930s at the Indian Statistical Institute.[1] The coefficient can be used to determine the relative closeness of the two samples being considered. It is used to measure the separability of classes in classification and it is considered to be more reliable than the Mahalanobis distance, as the Mahalanobis distance is a particular case of the Bhattacharyya distance when the standard deviations of the two classes are the same. Consequently, when two classes have similar means but different standard deviations, the Mahalanobis distance would tend to zero, whereas the Bhattacharyya distance grows depending on the difference between the standard deviations.
- In probability theory and statistics, the Jensen–Shannon divergence is a method of measuring the similarity between two probability distributions. It is also known as information radius (IRad)[1] or total divergence to the average.[2] It is based on the Kullback–Leibler divergence, with some notable (and useful) differences, including that it is symmetric and it always has a finite value. The square root of the Jensen–Shannon divergence is a metric often referred to as Jensen-Shannon distance.[3][4][5]
- In natural language processing (NLP) field, it is hard to augmenting text due to high complexity of language. Not every word we can replace it by others such as a, an, the. Also, not every word has synonym. Even changing a word, the context will be totally difference. On the other hand, generating augmented image in computer vision area is relative easier. Even introducing noise or cropping out portion of image, model can still classify the image.
- Document embeddings and sentence relatedness using BERT
- Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them. LSA itself is an unsupervised way of uncovering synonyms in a collection of documents. To start, we take a look how Latent Semantic Analysis is used in Natural Language Processing to analyze relationships between a set of documents and the terms that they contain. Then we go steps further to analyze and classify sentiment. We will review Chi Squared for feature selection along the way.

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