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}
Public repository of the 3DV 2021 paper "Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds"

Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds Björn Michele1), Alexandre Boulch1), Gilles Puy1), Maxime Bucher1) and Rena

valeo.ai 15 Dec 22, 2022
The story of Chicken for Club Bing

Chicken Story tl;dr: The time when Microsoft banned my entire country for cheating at Club Bing. (A lot of the details are from memory so I've recreat

Eyal 142 May 16, 2022
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without

sianchen 22 May 28, 2022
Collapse by Conditioning: Training Class-conditional GANs with Limited Data

Collapse by Conditioning: Training Class-conditional GANs with Limited Data Moha

Mohamad Shahbazi 33 Dec 06, 2022
Towards End-to-end Video-based Eye Tracking

Towards End-to-end Video-based Eye Tracking The code accompanying our ECCV 2020 publication and dataset, EVE. Authors: Seonwook Park, Emre Aksan, Xuco

Seonwook Park 76 Dec 12, 2022
A fast Protein Chain / Ligand Extractor and organizer.

Are you tired of using visualization software, or full blown suites just to separate protein chains / ligands ? Are you tired of organizing the mess o

Amine Abdz 9 Nov 06, 2022
Official repository for "PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation"

pair-emnlp2020 Official repository for the paper: Xinyu Hua and Lu Wang: PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long

Xinyu Hua 31 Oct 13, 2022
Official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo'

IterMVS official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo' Introduction IterMVS is a novel lear

Fangjinhua Wang 127 Jan 04, 2023
PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020).

NHDRRNet-PyTorch This is the PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020). 0. Differences between Original Paper and

Yutong Zhang 1 Mar 01, 2022
As-ViT: Auto-scaling Vision Transformers without Training

As-ViT: Auto-scaling Vision Transformers without Training [PDF] Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou In ICLR 2

VITA 68 Sep 05, 2022
ivadomed is an integrated framework for medical image analysis with deep learning.

Repository on the collaborative IVADO medical imaging project between the Mila and NeuroPoly labs.

144 Dec 19, 2022
Code & Data for the Paper "Time Masking for Temporal Language Models", WSDM 2022

Time Masking for Temporal Language Models This repository provides a reference implementation of the paper: Time Masking for Temporal Language Models

Guy Rosin 12 Jan 06, 2023
Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

HamasKhan 3 Jul 08, 2022
Code and models for "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", OmniCV Workshop @ CVPR21.

Pano3D A Holistic Benchmark and a Solid Baseline for 360o Depth Estimation Pano3D is a new benchmark for depth estimation from spherical panoramas. We

Visual Computing Lab, Information Technologies Institute, Centre for Reseach and Technology Hellas 50 Dec 29, 2022
Public implementation of "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression" from CoRL'21

Self-Supervised Reward Regression (SSRR) Codebase for CoRL 2021 paper "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression "

19 Dec 12, 2022
基于Flask开发后端、VUE开发前端框架,在WEB端部署YOLOv5目标检测模型

基于Flask开发后端、VUE开发前端框架,在WEB端部署YOLOv5目标检测模型

37 Jan 01, 2023
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

OpenPCDet OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. It is also the official code release o

OpenMMLab 3.2k Dec 31, 2022
Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images

Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K

Aitor Ruano 87 Dec 16, 2022
PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

Federated Learning with Non-IID Data This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vik

Youngjoon Lee 48 Dec 29, 2022