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

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
Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)

Debiasing Item-to-Item Recommendations With Small Annotated Datasets This is the code for our RecSys '20 paper. Other materials can be found here: Ful

Microsoft 34 Aug 10, 2022
3D AffordanceNet is a 3D point cloud benchmark consisting of 23k shapes from 23 semantic object categories, annotated with 56k affordance annotations and covering 18 visual affordance categories.

3D AffordanceNet This repository is the official experiment implementation of 3D AffordanceNet benchmark. 3D AffordanceNet is a 3D point cloud benchma

null 49 Dec 1, 2022
A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.

Knodle (Knowledge-supervised Deep Learning Framework) - a new framework for weak supervision with neural networks. It provides a modularization for se

null 93 Nov 6, 2022
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Overview PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein g

Shayne O'Brien 471 Dec 16, 2022
NUANCED is a user-centric conversational recommendation dataset that contains 5.1k annotated dialogues and 26k high-quality user turns.

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions Overview NUANCED is a user-centric conversational recommen

Facebook Research 18 Dec 28, 2021
Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation

TensorFlow White Paper Notes Features Notes broken down section by section, as well as subsection by subsection Relevant links to documentation, resou

Sam Abrahams 437 Oct 9, 2022
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection, AAAI 2021.

Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection This repository is an official implementation of the AAAI 2021 paper Co-mi

MEGVII Research 20 Dec 7, 2022
Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically.

Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically. The collected data will then be used to train a deep neural network that can detect enemy player models in real time, during gameplay. Finally, a virtual input device will adjust the player's crosshair based on live detections for greater accuracy.

Martin Valchev 3 Apr 24, 2022
CCPD: a diverse and well-annotated dataset for license plate detection and recognition

CCPD (Chinese City Parking Dataset, ECCV) UPdate on 10/03/2019. CCPD Dataset is now updated. We are confident that images in subsets of CCPD is much m

detectRecog 1.8k Dec 30, 2022
Reusable constraint types to use with typing.Annotated

annotated-types PEP-593 added typing.Annotated as a way of adding context-specific metadata to existing types, and specifies that Annotated[T, x] shou

null 125 Dec 26, 2022
Knowledge Management for Humans using Machine Learning & Tags

HyperTag HyperTag helps humans intuitively express how they think about their files using tags and machine learning.

Ravn Tech, Inc. 165 Nov 4, 2022
UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language This repository contains UA-GEC data and an accompanying Python lib

Grammarly 226 Dec 29, 2022
ERISHA is a mulitilingual multispeaker expressive speech synthesis framework. It can transfer the expressivity to the speaker's voice for which no expressive speech corpus is available.

ERISHA: Multilingual Multispeaker Expressive Text-to-Speech Library ERISHA is a multilingual multispeaker expressive speech synthesis framework. It ca

Ajinkya Kulkarni 43 Nov 27, 2022
Official repository for "Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems"

Action-Based Conversations Dataset (ABCD) This respository contains the code and data for ABCD (Chen et al., 2021) Introduction Whereas existing goal-

ASAPP Research 49 Oct 9, 2022
This repository contains the code for "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP".

Self-Diagnosis and Self-Debiasing This repository contains the source code for Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based

Timo Schick 62 Dec 12, 2022
Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation.

AVATAR Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation. AVATAR stands for jAVA-pyThon progrAm tRanslation. AV

Wasi Ahmad 26 Dec 3, 2022
[2021 MultiMedia] CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval

CONQUER: Contexutal Query-aware Ranking for Video Corpus Moment Retreival PyTorch implementation of CONQUER: Contexutal Query-aware Ranking for Video

Hou zhijian 23 Dec 26, 2022
Read and write layered TIFF ImageSourceData and ImageResources tags

Read and write layered TIFF ImageSourceData and ImageResources tags Psdtags is a Python library to read and write the Adobe Photoshop(r) specific Imag

Christoph Gohlke 4 Feb 5, 2022
darija <-> english dictionary

darija-dictionary Having advanced IT solutions that are well adapted to the Moroccan context passes inevitably through understanding Moroccan dialect.

DODa 102 Jan 1, 2023