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
Regression Metrics Calculation Made easy for tensorflow2 and scikit-learn

Regression Metrics Installation To install the package from the PyPi repository you can execute the following command: pip install regressionmetrics I

Ashish Patel 11 Dec 16, 2022
NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows

NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows This repo contains the code for the paper Tractable Densit

Layer6 Labs 4 Dec 12, 2022
OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

🔥 DrugOOD 🔥 : OOD Dataset Curator and Benchmark for AI Aided Drug Discovery This is the official implementation of the DrugOOD project, this is the

108 Dec 17, 2022
[ICCV 2021] Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

MAED: Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation Getting Started Our codes are implemented and tested with pyth

ZiNiU WaN 176 Dec 15, 2022
PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds

Pseudo Numerical Methods for Diffusion Models on Manifolds (PNDM) This repo is the official PyTorch implementation for the paper Pseudo Numerical Meth

Luping Liu (刘路平) 196 Jan 05, 2023
Codeflare - Scale complex AI/ML pipelines anywhere

Scale complex AI/ML pipelines anywhere CodeFlare is a framework to simplify the integration, scaling and acceleration of complex multi-step analytics

CodeFlare 169 Nov 29, 2022
SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity

SSD: Single Shot MultiBox Detector Introduction Here is my pytorch implementation of 2 models: SSD-Resnet50 and SSDLite-MobilenetV2.

Viet Nguyen 149 Jan 07, 2023
Finding an Unsupervised Image Segmenter in each of your Deep Generative Models

Finding an Unsupervised Image Segmenter in each of your Deep Generative Models Description Recent research has shown that numerous human-interpretable

Luke Melas-Kyriazi 61 Oct 17, 2022
Content shared at DS-OX Meetup

Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja

Arvindra 69 Dec 23, 2022
Using deep learning to predict gene structures of the coding genes in DNA sequences of Arabidopsis thaliana

DeepGeneAnnotator: A tool to annotate the gene in the genome The master thesis of the "Using deep learning to predict gene structures of the coding ge

Ching-Tien Wang 3 Sep 09, 2022
Scripts and outputs related to the paper Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings.

Knowledge Graph Embeddings and Chemical Effect Prediction, 2020. Scripts and outputs related to the paper Prediction of Adverse Biological Effects of

Knowledge Graphs at the Norwegian Institute for Water Research 1 Nov 01, 2021
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022
MLP-Like Vision Permutator for Visual Recognition (PyTorch)

Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition (arxiv) This is a Pytorch implementation of our paper. We present Vision

Qibin (Andrew) Hou 162 Nov 28, 2022
GradAttack is a Python library for easy evaluation of privacy risks in public gradients in Federated Learning

GradAttack is a Python library for easy evaluation of privacy risks in public gradients in Federated Learning, as well as corresponding mitigation strategies.

129 Dec 30, 2022
Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network.

Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network

111 Dec 27, 2022
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer

Spotify 10.6k Jan 04, 2023
HAT: Hierarchical Aggregation Transformers for Person Re-identification

HAT: Hierarchical Aggregation Transformers for Person Re-identification

11 Sep 05, 2022
Automatic Differentiation Multipole Moment Molecular Forcefield

Automatic Differentiation Multipole Moment Molecular Forcefield Performance notes On a single gpu, using waterbox_31ang.pdb example from MPIDplugin wh

4 Jan 07, 2022
A collection of resources on GAN Inversion.

This repo is a collection of resources on GAN inversion, as a supplement for our survey

Semi-Supervised Signed Clustering Graph Neural Network (and Implementation of Some Spectral Methods)

SSSNET SSSNET: Semi-Supervised Signed Network Clustering For details, please read our paper. Environment Setup Overview The project has been tested on

Yixuan He 9 Nov 24, 2022