Semantic graph parser based on Categorial grammars

Overview

Lambekseq

semgraph

"Everyone who failed Greek or Latin hates it."


This package is for proving theorems in Categorial grammars (CG) and constructing semantic graphs, i.e., semgraphs on top of that.

Three CG calculuses are supported here (see below). A "proof" is simply a set of atom links, abstracting away from derivaiton details.

Requirements

Add the path to the package to PYTHONPATH. None of the below packages is needed to use the theorem proving facility.

Semantic graphs derive from digraph:

For graph visualization we use

Background

This package is used for the author's PhD thesis in progress.

Categorial grammars:

Semantic graphs:

Theorem Proving

To prove a theorem, use atomlink module. For example, using Lambek Calculus to prove np np\s -> s.

>>> import lambekseq.atomlink as al

>>> con, *pres = 's np np\\s'.split()
>>> con, pres, parser, _ = al.searchLinks(al.LambekProof, con, pres)
>>> al.printLinks(con, pres, parser)

This outputs

----------
s_0 <= np_1 np_2\s_3

(np_1, np_2), (s_0, s_3)

Total: 1

You can run atomlink in command line. The following finds proofs for the theorems in input, using abbreviation definitions in abbr.json and Contintuized CCG.

$ python atomlink.py -i input -a abbr.json -c ccg --earlyCollapse

Theorem s qp vp/s qp vp (the first item is the conclusion, the rest the premises) is thus proved as follows:

<class 'lambekseq.cntccg.Cntccg'>
----------
s_0 <= (s_1^np_2)!s_3 (np_4\s_5)/s_6 (s_7^np_8)!s_9 np_10\s_11

(np_10, np_8), (np_2, np_4), (s_0, s_3), (s_1, s_5), (s_11, s_7), (s_6, s_9)

Total: 1

When using Lambek/Displacement/CCG calculus, you can also inspect the proof tree that yields atom links:

>>> con, *pres = 's', 'np', '(np\\s)/np', 'np'
>>> con, pres, parser, _ = al.searchLinks(al.LambekProof, con, pres)
>>> parser.buildTree()
>>> parser.printTree()
(np_1, np_2), (np_4, np_5), (s_0, s_3)
........ s_3 -> s_0
........ np_1 -> np_2
.... np_1 np_2\s_3 -> s_0
.... np_5 -> np_4
 np_1 (np_2\s_3)/np_4 np_5 -> s_0

You can export the tree to Bussproofs code for Latex display:

bussproof

>>> print(parser.bussproof)
...
\begin{prooftree}
\EnableBpAbbreviations
        \AXC{s$_{3}$ $\to$ s$_{0}$}
        \AXC{np$_{1}$ $\to$ np$_{2}$}
    \BIC{np$_{1}$\enskip{}np$_{2}$\textbackslash s$_{3}$ $\to$ s$_{0}$}
    \AXC{np$_{5}$ $\to$ np$_{4}$}
\BIC{np$_{1}$\enskip{}(np$_{2}$\textbackslash s$_{3}$)/np$_{4}$\enskip{}np$_{5}$ $\to$ s$_{0}$}
\end{prooftree}

Run python atomlink.py --help for details.

Semantic Parsing

Use semcomp module for semantic parsing. You need to define graph schemata for parts of speech as in schema.json.

>>> from lambekseq.semcomp import SemComp
>>> SemComp.load_lexicon(abbr_path='abbr.json',
                         vocab_path='schema.json')
>>> ex = 'a boy walked a dog'
>>> pos = 'ind n vt ind n'
>>> sc = SemComp(zip(ex.split(), pos.split()), calc='dsp')
>>> sc.unify('s')

Use graphviz's Source to display the semgraphs constructed from the input:

>>> from graphviz import Source
>>> Source(sc.semantics[0].dot_styled)

This outputs
a boy walked a dog

You can inspect the syntax behind this parse:

>>> sc.syntax[0].insight.con, sc.syntax[0].insight.pres
('s_0', ['np_1/n_2', 'n_3', '(np_4\\s_5)/np_6', 'np_7/n_8', 'n_9'])

>>> sc.syntax[0].links
['(n_2, n_3)', '(n_8, n_9)', '(np_1, np_4)', '(np_6, np_7)', '(s_0, s_5)']

See demo/demo.ipynb for more examples.

You can export semgraphs to tikz code that can be visually edited by TikZit.

a boy walked a dog

>>> print(sc.semantics[0].tikz)
\begin{tikzpicture}
\begin{pgfonlayer}{nodelayer}
        \node [style=node] (i1) at (-1.88,2.13) {};
        \node [style=none] (g2u0) at (-2.99,3.07) {};
        \node [style=node] (i0) at (0.99,-2.68) {};
        \node [style=none] (g5u0) at (1.09,-4.13) {};
        \node [style=node] (g3a0) at (0.74,0.43) {};
        \node [style=none] (g3u0) at (2.05,1.19) {};
        \node [style=none] (0) at (-3.04,2.89) {boy};
        \node [style=none] (1) at (0.61,-4.00) {dog};
        \node [style=none] (2) at (-0.66,0.72) {ag};
        \node [style=none] (3) at (0.63,-0.77) {th};
        \node [style=none] (4) at (2.42,1.09) {walked};
\end{pgfonlayer}
\begin{pgfonlayer}{edgelayer}
        \draw [style=arrow] (i1) to (g2u0.center);
        \draw [style=arrow] (i0) to (g5u0.center);
        \draw [style=arrow] (g3a0) to (i1);
        \draw [style=arrow] (g3a0) to (i0);
        \draw [style=arrow] (g3a0) to (g3u0.center);
\end{pgfonlayer}
\end{tikzpicture}
Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization

Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization Code for reproducing our results in the Head2Toe paper. Paper: arxiv.or

Google Research 62 Dec 12, 2022
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT

LightHuBERT LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT | Github | Huggingface | SUPER

WangRui 46 Dec 29, 2022
Automated Attendance Project Using Face Recognition

dependencies for project: cmake 3.22.1 dlib 19.22.1 face-recognition 1.3.0 openc

Rohail Taha 1 Jan 09, 2022
TagLab: an image segmentation tool oriented to marine data analysis

TagLab: an image segmentation tool oriented to marine data analysis TagLab was created to support the activity of annotation and extraction of statist

Visual Computing Lab - ISTI - CNR 49 Dec 29, 2022
Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag

Zalando Research 120 Dec 24, 2022
This repository contains the source code for the paper "DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks",

DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks Project Page | Video | Presentation | Paper | Data L

Facebook Research 281 Dec 22, 2022
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth [Paper]

Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth [Paper] Downloads [Downloads] Trained ckpt files for NYU Depth V2 and

98 Jan 01, 2023
source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics This work will be published in Nature Biomedical

International Business Machines 71 Nov 15, 2022
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI'22)

ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022) Official code of the paper ShuttleN

Wei-Yao Wang 11 Nov 30, 2022
Final project for Intro to CS class.

Financial Analysis Web App https://share.streamlit.io/mayurk1/fin-web-app-final-project/webApp.py 1. Project Description This project is a technical a

Mayur Khanna 1 Dec 10, 2021
IndoNLI: A Natural Language Inference Dataset for Indonesian

IndoNLI: A Natural Language Inference Dataset for Indonesian This is a repository for data and code accompanying our EMNLP 2021 paper "IndoNLI: A Natu

15 Feb 10, 2022
Code for the paper "Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds" (ICCV 2021)

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds This is the official code implementation for the paper "Spatio-temporal Se

Hesper 63 Jan 05, 2023
《Towards High Fidelity Face Relighting with Realistic Shadows》(CVPR 2021)

Towards High Fidelity Face-Relighting with Realistic Shadows Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu. In CVPR, 2021. T

114 Dec 10, 2022
Implementation of Convolutional enhanced image Transformer

CeiT : Convolutional enhanced image Transformer This is an unofficial PyTorch implementation of Incorporating Convolution Designs into Visual Transfor

Rishikesh (ऋषिकेश) 82 Dec 13, 2022
Notepy is a full-featured Notepad Python app

Notepy A full featured python text-editor Notable features Autocompletion for parenthesis and quote Auto identation Syntax highlighting Compile and ru

Mirko Rovere 11 Sep 28, 2022
PyTorch common framework to accelerate network implementation, training and validation

pytorch-framework PyTorch common framework to accelerate network implementation, training and validation. This framework is inspired by works from MML

Dongliang Cao 3 Dec 19, 2022
PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation.

Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks (ICCV 2021) This repository is the official implem

71 Jan 04, 2023
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion

A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion This repo intends to release code for our work: Zhaoyang Lyu*, Zhifeng

Zhaoyang Lyu 68 Jan 03, 2023
Instance Semantic Segmentation List

Instance Semantic Segmentation List This repository contains lists of state-or-art instance semantic segmentation works. Papers and resources are list

bighead 87 Mar 06, 2022
All the essential resources and template code needed to understand and practice data structures and algorithms in python with few small projects to demonstrate their practical application.

Data Structures and Algorithms Python INDEX 1. Resources - Books Data Structures - Reema Thareja competitiveCoding Big-O Cheat Sheet DAA Syllabus Inte

Shushrut Kumar 129 Dec 15, 2022