PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

Related tags

Deep Learningpastrie
Overview

PASTRIE

CC BY-SA 4.0

Official release of the corpus described in the paper:

Michael Kranzlein, Emma Manning, Siyao Peng, Shira Wein, Aryaman Arora, and Nathan Schneider (2020). PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English [link]. Proceedings of the 14th Linguistic Annotation Workshop.


Overview

PASTRIE is a corpus of English data from Reddit annotated with preposition supersenses from the SNACS inventory.

While the data in PASTRIE is in English, it was produced by presumed speakers of four L1s:

  • English
  • French
  • German
  • Spanish

For details on how L1s were identified, see section 3.1 of Rabinovich et al. (2018).

Annotation Example

Below is an example sentence from the corpus, where annotation targets are bolded and preposition supersenses are annotated with the notation SceneRole↝Function. Together, a scene role and function are known as a construal.


Data Formats

PASTRIE is released in the following formats. We expect that most projects will be best served by one of the JSON formats.

  • .conllulex: the 19-column CoNLL-U-Lex format originally used for STREUSLE.
  • .json: a JSON representation of the CoNLL-U-Lex that does not require a CoNLL-U-Lex parser.
  • .govobj.json: an extended version of the JSON representation that contains information about each preposition's syntactic parent and object.

PASTRIE mostly follows STREUSLE with respect to the data format and SNACS annotation practice. Primary differences in the annotations are:

  • Lemmas, part-of-speech tags, and syntactic dependencies aim to follow the UD standard in both cases. They are gold in STREUSLE, versus automatic with some manual corrections in PASTRIE.
    • PASTRIE does not group together base+clitic combinations, whereas STREUSLE does (multiword tokens—where a single orthographic word contains multiple syntactic words).
    • PASTRIE does not regularly specify SpaceAfter=No to indicate alignment between the tokens and the raw text.
    • In PASTRIE, the raw text string accompanying the sentence may contain two or more consecutive spaces.
    • PASTRIE lacks enhanced dependencies.
  • Multiword expression annotations in PASTRIE are limited to expressions containing a preposition. Depending on the syntactic head, the expression may or may not have a SNACS supersense.
    • Verbal multiword expressions in PASTRIE are not subtyped in the lexcat; they all have a lexcat of V.
  • Noun and verb expressions in PASTRIE do not have supersense labels.
Comments
  • Misc. annotation errors and/or conversion script bugs

    Misc. annotation errors and/or conversion script bugs

    There are some annotations which I'm fairly sure are incorrect and are choking up the JSON conversion script. (These errors occur using the unmodified versions of all scripts taken straight from STRUESLE.) One or two might also be indicative of a bug in the conllulex2json.py file.

    1. vs mistagged as a noun--should be prep

    AssertionError: ('french-fad32caf-e595-e3cb-07bf-aaea891e53cb-02', {'lexlemma': 'versus', 'lexcat': 'CCONJ', 'ss': 'c', 'ss2': 'c', 'toknums': [3]}, {'#': 3, 'word': 'vs', 'lemma': 'versus', 'upos': 'NOUN', 'xpos': 'NN', 'feats': None, 'head': 8, 'deprel': 'nsubj', 'edeps': None, 'misc': None, 'smwe': None, 'wmwe': None, 'lextag': 'O-CCONJ-`c'})

    1. ditto

    AssertionError: ('french-fad32caf-e595-e3cb-07bf-aaea891e53cb-02', {'lexlemma': 'versus', 'lexcat': 'CCONJ', 'ss': 'c', 'ss2': 'c', 'toknums': [3]}, {'#': 3, 'word': 'vs', 'lemma': 'versus', 'upos': 'NOUN', 'xpos': 'NN', 'feats': None, 'head': 8, 'deprel': 'nsubj', 'edeps': None, 'misc': None, 'smwe': None, 'wmwe': None, 'lextag': 'O-CCONJ-`c'})

    1. Script complains about "to" in this snippet at ID=23. Not immediately clear to me what the issue is--perhaps that "to" is labeled ADP/IN? For its xpos I think it ought to be TO, not sure about its upos. Snippet:
    13      shit    shit    NOUN    NN      _       16      obl:npmod       _       _       _       _       _       _       _       _       _       _       _
    14      this    this    PRON    DT      _       16      nsubj   _       _       _       _       _       _       _       _       _       _       _
    15      can     can     AUX     MD      _       16      aux     _       _       _       _       _       _       _       _       _       _       _
    16      end     end     VERB    VB      _       4       parataxis       _       _       _       _       _       _       _       _       _       _       _
    17      right   right   ADV     RB      _       18      advmod  _       _       _       _       _       _       _       _       _       _       _
    18      now     now     ADV     RB      _       16      advmod  _       _       _       _       _       _       _       _       _       _       _
    19      if      if      SCONJ   IN      _       21      mark    _       _       _       _       _       _       _       _       _       _       _
    20      I       I       PRON    PRP     _       21      nsubj   _       _       _       _       _       _       _       _       _       _       _
    21      want    want    VERB    VBP     _       16      advcl   _       _       _       _       _       _       _       _       _       _       _
    22      it      it      PRON    PRP     _       21      obj     _       _       _       _       _       _       _       _       _       _       _
    23      to      to      ADP     IN      _       21      obl     _       _       _       _       _       `i      `i      _       _       _       _
    24      .       .       PUNCT   .       _       4       punct   _       _       _       _       _       _       _       _       _       _       _
    

    Error:

    AssertionError: ('french-a17a4340-f9c0-8fef-fa1b-1bf13879399b-02', {'lexlemma': 'to', 'lexcat': 'INF', 'ss': 'i', 'ss2': 'i', 'toknums': [23]}, {'#': 23, 'word': 'to', 'lemma': 'to', 'upos': 'ADP', 'xpos': 'IN', 'feats': None, 'head': 21, 'deprel': 'obl', 'edeps': None, 'misc': None, 'smwe': None, 'wmwe': None, 'lextag': 'O-INF-`i'})

    Relevant span of code:

                if validate_pos and upos!=lc and (upos,lc) not in {('NOUN','N'),('PROPN','N'),('VERB','V'),
                    ('ADP','P'),('ADV','P'),('SCONJ','P'),
                    ('ADP','DISC'),('ADV','DISC'),('SCONJ','DISC'),
                    ('PART','POSS')}:
                    # most often, the single-word lexcat should match its upos
                    # check a list of exceptions
                    mismatchOK = False
                    if xpos=='TO' and lc.startswith('INF'):
                        mismatchOK = True
                    elif (xpos=='TO')!=lc.startswith('INF'):
                        assert upos in ['SCONJ', "ADP"] and swe['lexlemma']=='for',(sent['sent_id'],swe,tok)
                        mismatchOK = True
    
    1. Originator as function:

    (in french-c02823ec-60bd-adce-7327-01337eb9d1c8-02) AssertionError: ('p.Originator should never be function', {'lexlemma': 'you', 'lexcat': 'PRON.POSS', 'ss': 'p.Originator', 'ss2': 'p.Originator', 'toknums': [1]})

    1. lexcat DISC with ADJ:

    AssertionError: In spanish-a25e8289-e04a-f5af-ce56-ead9faca65b1-02, single-word expression 'like' has lexcat DISC, which is incompatible with its upos ADJ

    1. "her" tagged with Possessor is incorrectly parsed as iobj and tagged as PRP instead of PRP$. Relevant snippet:
    1       My      my      PRON    PRP$    _       2       nmod:poss       _       _       _       _       _       SocialRel       Gestalt _       _       _       _
    2       grandma grandma NOUN    NN      _       3       nsubj   _       _       _       _       _       _       _       _       _       _       _
    3       had     have    VERB    VBD     _       0       root    _       _       _       _       _       _       _       _       _       _       _
    4       her     she     PRON    PRP     _       3       iobj    _       _       _       _       _       Possessor       Possessor       _       _       _       _
    5       super   super   ADV     RB      _       6       advmod  _       _       _       _       _       _       _       _       _       _       _
    6       thick   thick   ADJ     JJ      _       8       amod    _       _       _       _       _       _       _       _       _       _       _
    7       floor   floor   NOUN    NN      _       8       compound        _       _       _       _       _       _       _       _       _       _       _
    8       mats    mat     NOUN    NNS     _       3       obj     _       _       _       _       _       _       _       _       _       _       _
    9       *       *       PUNCT   NFP     _       8       punct   _       _       _       _       _       _       _       _       _       _       _
    10      over    over    ADP     IN      _       13      case    _       _       _       _       _       Locus   Locus   _       _       _       _
    11      *       *       PUNCT   NFP     _       13      punct   _       _       _       _       _       _       _       _       _       _       _
    12      the     the     DET     DT      _       13      det     _       _       _       _       _       _       _       _       _       _       _
    13      accelerator     accelerator     NOUN    NN      _       3       obl     _       _       _       _       _       _       _       _       _       _       _
    14      ,       ,       PUNCT   ,       _       3       punct   _       _       _       _       _       _       _       _       _       _       _
    

    Error:

    AssertionError: In spanish-ebba3c73-2431-c216-8f4d-d469ee8d5564-01, single-word expression 'her' has lexcat P, which is incompatible with its upos PRON

    1. "NA" is misannotated--this is NA as in North America, i.e. a PROPN/NP, but it's lemmatized as "no", and its tags are weird.

    AssertionError: ('german-35000895-1d78-c18a-01ed-f7410b9c0581-01', {'lexlemma': 'no', 'lexcat': 'ADV', 'ss': None, 'ss2': None, 'toknums': [5]}, {'#': 5, 'word': 'NA', 'lemma': 'no', 'upos': 'PART', 'xpos': 'TO', 'feats': None, 'head': 6, 'deprel': 'mark', 'edeps': None, 'misc': None, 'smwe': None, 'wmwe': None, 'lextag': 'O-ADV'})

    opened by lgessler 6
  • Prepositional supersense annotations on non-preposition targets

    Prepositional supersense annotations on non-preposition targets

    Is it OK for a verb-headed SMWE to have a prepositional supersense? The validator complains about it. Offending SMWE:

    21	give	give	VERB	VB	_	10	conj	_	_	2:1	_	give up on	p.Theme	p.Theme	_	_	_	_
    22	up	up	ADP	RP	_	21	compound:prt	_	_	2:2	_	_	_	_	_	_	_	_
    23	on	on	ADP	IN	_	24	case	_	_	2:3	_	_	_	_	_	_	_	_
    
    opened by lgessler 5
  • Prepositions unannotated for supersense

    Prepositions unannotated for supersense

    Token 6:

    # sent_id = french-f57dd6ab-5263-4c8a-e360-8ec683e6a37a-02
    # text = Once you have the hang of it it s pretty fast ( and does n't eat your clutch ) .
    1	Once	once	SCONJ	IN	_	3	mark	_	_	_	_	_	_	_	_	_	_	_
    2	you	you	PRON	PRP	_	3	nsubj	_	_	_	_	_	_	_	_	_	_	_
    3	have	have	VERB	VBP	_	11	advcl	_	_	_	_	_	_	_	_	_	_	_
    4	the	the	DET	DT	_	5	det	_	_	_	_	_	_	_	_	_	_	_
    5	hang	hang	NOUN	NN	_	3	obj	_	_	_	_	_	_	_	_	_	_	_
    6	of	of	ADP	IN	_	7	case	_	_	_	_	_	_	_	_	_	_	_
    7	it	it	PRON	PRP	_	5	nmod	_	_	_	_	_	_	_	_	_	_	_
    8	it	it	PRON	PRP	_	11	nsubj	_	_	_	_	_	_	_	_	_	_	_
    9	s	be	AUX	VBZ	_	11	cop	_	_	_	_	_	_	_	_	_	_	_
    10	pretty	pretty	ADV	RB	_	11	advmod	_	_	_	_	_	_	_	_	_	_	_
    11	fast	fast	ADJ	JJ	_	0	root	_	_	_	_	_	_	_	_	_	_	_
    12	(	(	PUNCT	-LRB-	_	16	punct	_	_	_	_	_	_	_	_	_	_	_
    13	and	and	CCONJ	CC	_	16	cc	_	_	_	_	_	_	_	_	_	_	_
    14	does	do	AUX	VBZ	_	16	aux	_	_	_	_	_	_	_	_	_	_	_
    15	n't	not	PART	RB	_	16	advmod	_	_	_	_	_	_	_	_	_	_	_
    16	eat	eat	VERB	VB	_	11	conj	_	_	_	_	_	_	_	_	_	_	_
    17	your	you	PRON	PRP$	_	18	nmod:poss	_	_	_	_	_	Possessor	Possessor	_	_	_	_
    18	clutch	clutch	NOUN	NN	_	16	obj	_	_	_	_	_	_	_	_	_	_	_
    19	)	)	PUNCT	-RRB-	_	11	punct	_	_	_	_	_	_	_	_	_	_	_
    20	.	.	PUNCT	.	_	11	punct	_	_	_	_	_	_	_	_	_	_	_
    

    I assumed that all preps were supposed to be annotated, but perhaps not?

    opened by lgessler 3
  • Apostrophes removed in preprocessing?

    Apostrophes removed in preprocessing?

    Looking through the data, there are a LOT of sentences where clitics are tokenized off but lack an apostrophe. Is that just the genre or did they get lost in preprocessing?

    opened by nschneid 2
  • Dataset requested

    Dataset requested

    Hi all,

    I would like to request the PASTRIE dataset accompanying the paper "PASTRIE: A Corpus of Prepositions Annotated with Supsersense Tags in Reddit International English".

    Thanks for reply.

    opened by fj-morales 2
  • SNACS supersense tags should start with

    SNACS supersense tags should start with "p."

    For compatibility with STREUSLE, it should be p.Locus, p.Theme, etc.

    Special labels like `i `d `c `$ ?? should not start with p.. In fact, the backtick labels from annotation are not represented as such in STREUSLE—they are reflected in the LEXCAT column of the data.

    opened by nschneid 0
  • Questionable adpositional MWEs

    Questionable adpositional MWEs

    • in_male_term — from "in male terms"; should be in_term (at most)
    • in_the_first_place
    • in_my_hand — from "in my hands"; should be in_hand (at most)
    • for_quite_some_time — just Duration for, weak MWE?
    • at_all_time — from what should have been "at all times". OK?
    • on_a_smaller_scale — omit adjective?
    • withouth — typo
    • see_as — "seeing as" (deverbal MWE acting like a preposition)
    opened by nschneid 0
  • Some undersegmentation of sentences

    Some undersegmentation of sentences

    Despite manual editing there are still places where a long sentence ought to be split up (esp. where it consists of a blockquoted sentence with > followed by a response). Looking for multiple consecutive spaces in the raw text uncovers some of these (as well as some discourse appendages like emoticons, which should probably remain in the same UD sentence).

    It would be nice to write a script to help clean these up—the tricky part is updating offsets in each parse.

    opened by nschneid 0
Releases(v2.0.1)
  • v2.0.1(Nov 21, 2021)

  • v2.0(Oct 22, 2021)

    • Switch to full .conllulex format following STREUSLE
      • add lexcats (#3), morphological features, newdoc directives
    • Scripts for validation and format conversion
    • Clean up various annotation issues, including:
      • restore apostrophes and fixing other conversion problems (#6, #9)
      • include pretokenized raw text (#12)
    Source code(tar.gz)
    Source code(zip)
  • v1.0.1(Dec 14, 2020)

    • Added .json file format
    • Switched lemmatization and pos tagging from StanfordNLP 0.2.0 to Stanza 1.1.1
    • Corrected rare encoding issue from v1.0
    Source code(tar.gz)
    Source code(zip)
Owner
NERT @ Georgetown
NERT @ Georgetown
A pyparsing-based library for parsing SOQL statements

CONTRIBUTORS WANTED!! Installation pip install python-soql-parser or, with poetry poetry add python-soql-parser Usage from python_soql_parser import p

Kicksaw 0 Jun 07, 2022
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?

Think Big, Teach Small: Do Language Models Distil Occam’s Razor? Software related to the paper "Think Big, Teach Small: Do Language Models Distil Occa

0 Dec 07, 2021
Hardware accelerated, batchable and differentiable optimizers in JAX.

JAXopt Installation | Examples | References Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX. Installation JAXopt can be

Google 621 Jan 08, 2023
A small fun project using python OpenCV, mediapipe, and pydirectinput

Here I tried a small fun project using python OpenCV, mediapipe, and pydirectinput. Here we can control moves car game when yellow color come to right box (press key 'd') left box (press key 'a') lef

Sameh Elisha 3 Nov 17, 2022
Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous Event-Based Data"

A Differentiable Recurrent Surface for Asynchronous Event-Based Data Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous

Marco Cannici 21 Oct 05, 2022
Code for "MetaMorph: Learning Universal Controllers with Transformers", Gupta et al, ICLR 2022

MetaMorph: Learning Universal Controllers with Transformers This is the code for the paper MetaMorph: Learning Universal Controllers with Transformers

Agrim Gupta 50 Jan 03, 2023
Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction, ICCV-2021".

HF2-VAD Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictio

76 Dec 21, 2022
This repository contains the PyTorch implementation of the paper STaCK: Sentence Ordering with Temporal Commonsense Knowledge appearing at EMNLP 2021.

STaCK: Sentence Ordering with Temporal Commonsense Knowledge This repository contains the pytorch implementation of the paper STaCK: Sentence Ordering

Deep Cognition and Language Research (DeCLaRe) Lab 23 Dec 16, 2022
Spatial-Location-Constraint-Prototype-Loss-for-Open-Set-Recognition

Spatial Location Constraint Prototype Loss for Open Set Recognition Official PyTorch implementation of "Spatial Location Constraint Prototype Loss for

Xia Ziheng 12 Jun 24, 2022
[UNMAINTAINED] Automated machine learning for analytics & production

auto_ml Automated machine learning for production and analytics Installation pip install auto_ml Getting started from auto_ml import Predictor from au

Preston Parry 1.6k Jan 02, 2023
Meandering In Networks of Entities to Reach Verisimilar Answers

MINERVA Meandering In Networks of Entities to Reach Verisimilar Answers Code and models for the paper Go for a Walk and Arrive at the Answer - Reasoni

Shehzaad Dhuliawala 271 Dec 13, 2022
Open source person re-identification library in python

Open-ReID Open-ReID is a lightweight library of person re-identification for research purpose. It aims to provide a uniform interface for different da

Tong Xiao 1.3k Jan 01, 2023
Official PyTorch implementation of GDWCT (CVPR 2019, oral)

This repository provides the official code of GDWCT, and it is written in PyTorch. Paper Image-to-Image Translation via Group-wise Deep Whitening-and-

WonwoongCho 135 Dec 02, 2022
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

FusionNet_Pytorch FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics Requirements Pytorch 0.1.11 Pyt

Choi Gunho 102 Dec 13, 2022
USAD - UnSupervised Anomaly Detection on multivariate time series

USAD - UnSupervised Anomaly Detection on multivariate time series Scripts and utility programs for implementing the USAD architecture. Implementation

116 Jan 04, 2023
A synthetic texture-invariant dataset for object detection of UAVs

A synthetic dataset for object detection of UAVs This repository contains a synthetic datasets accompanying the paper Sim2Air - Synthetic aerial datas

LARICS Lab 10 Aug 13, 2022
Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit

STORM Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit [Install Instructions] [Paper] [Website] This package contains code

NVIDIA Research Projects 101 Dec 12, 2022
PyTorch implementation of Convolutional Neural Fabrics http://arxiv.org/abs/1606.02492

PyTorch implementation of Convolutional Neural Fabrics arxiv:1606.02492 There are some minor differences: The raw image is first convolved, to obtain

Anuvabh Dutt 25 Dec 22, 2021
A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Requirements pytorch 1.1+ torchvision 0.3+ pyclipper opencv3 gcc

zhoujun 400 Dec 26, 2022
UniFormer - official implementation of UniFormer

UniFormer This repo is the official implementation of "Uniformer: Unified Transf

SenseTime X-Lab 573 Jan 04, 2023