Chomsky-Schützenberger parsing for weighted multiple context-free languages

Denkinger, Tobias

doi:10.15398/jlm.v5i1.159

Cited by 2 publications

(2 citation statements)

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“…The choice to use web technologies was made to take advantage of ready-to-use user interface elements, including the D3 visualization library and its pre-implemented zooming, panning, and dragging features. For the FFT computation, we use a 2014 version of the Project Nayuki Javascript FFT library ( Nayuki ), adapted by Cannam ( Cannam ). The autocorrelation computation does not use any libraries and is done directly in-app.…”

Section: Methodsmentioning

confidence: 99%

SlowMoMan: a web app for discovery of important features along user-drawn trajectories in 2D embeddings

Deol,

Weber,

2024

Bioinformatics Advances

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Motivation Nonlinear low-dimensional embeddings allow humans to visualize high-dimensional data, as is often seen in bioinformatics, where datasets may have tens of thousands of dimensions. However, relating the axes of a nonlinear embedding to the original dimensions is a nontrivial problem. In particular, humans may identify patterns or interesting subsections in the embedding, but cannot easily identify what those patterns correspond to in the original data. Results Thus, we present SlowMoMan (SLOW Motions on MANifolds), a web application which allows the user to draw a one-dimensional path onto a 2D embedding. Then, by back-projecting the manifold to the original, high-dimensional space, we sort the original features such that those most discriminative along the manifold are ranked highly. We show a number of pertinent use cases for our tool, including trajectory inference, spatial transcriptomics, and automatic cell classification. Availability and implementation Software: https://yunwilliamyu.github.io/SlowMoMan/; Code: https://github.com/yunwilliamyu/SlowMoMan.

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Section: Methodsmentioning

confidence: 99%

SlowMoMan: a web app for discovery of important features along user-drawn trajectories in 2D embeddings

Deol,

Weber,

2024

Bioinformatics Advances

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“…Chomsky-Schützenberger (short: CS) parsing was introduced by Hulden (2011) for CFGs and extended to MCFGs by Denkinger (2017).…”

Section: Introductionmentioning

confidence: 99%

Implementation of a

Ruprecht¹,

Denkinger²

2019

Proceedings of the 2019 Conference of the North

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Constituent parsing has been studied extensively in the last decades. Chomsky-Schützenberger parsing as an approach to constituent parsing has only been investigated theoretically, yet. It uses the decomposition of a language into a regular language, a homomorphism, and a bracket language to divide the parsing problem into simpler subproblems. We provide the first implementation of Chomsky-Schützenberger parsing. It employs multiple context-free grammars and incorporates many refinements to achieve feasibility. We compare its performance to state-of-the-art grammar-based parsers.

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Chomsky-Schützenberger parsing for weighted multiple context-free languages

Abstract: We prove a Chomsky-Schützenberger representation theorem for multiple context-free languages weighted over complete commutative strong bimonoids. Using this representation we devise a parsing algorithm for a restricted form of those devices.

Cited by 2 publications

References 30 publications

SlowMoMan: a web app for discovery of important features along user-drawn trajectories in 2D embeddings

SlowMoMan: a web app for discovery of important features along user-drawn trajectories in 2D embeddings

Implementation of a

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