Search Results for author: Nathan Schneider

Found 78 papers, 22 papers with code

Subcategorizing Adverbials in Universal Conceptual Cognitive Annotation

no code implementations EMNLP (LAW, DMR) 2021 Zhuxin Wang, Jakob Prange, Nathan Schneider

Universal Conceptual Cognitive Annotation (UCCA) is a semantic annotation scheme that organizes texts into coarse predicate-argument structure, offering broad coverage of semantic phenomena.

Classifying Divergences in Cross-lingual AMR Pairs

1 code implementation EMNLP (LAW, DMR) 2021 Shira Wein, Nathan Schneider

Translation divergences are varied and widespread, challenging approaches that rely on parallel text.

Translation

Sprucing up Supersenses: Untangling the Semantic Clusters of Accompaniment and Purpose

no code implementations COLING (LAW) 2020 Jena D. Hwang, Nathan Schneider, Vivek Srikumar

We reevaluate an existing adpositional annotation scheme with respect to two thorny semantic domains: accompaniment and purpose.

Putting Words in BERT’s Mouth: Navigating Contextualized Vector Spaces with Pseudowords

1 code implementation EMNLP 2021 Taelin Karidi, Yichu Zhou, Nathan Schneider, Omri Abend, Vivek Srikumar

We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses.

K-SNACS: Annotating Korean Adposition Semantics

no code implementations DMR (COLING) 2020 Jena D. Hwang, Hanwool Choe, Na-Rae Han, Nathan Schneider

While many languages use adpositions to encode semantic relationships between content words in a sentence (e. g., agentivity or temporality), the details of how adpositions work vary widely across languages with respect to both form and meaning.

A Balanced and Broadly Targeted Computational Linguistics Curriculum

no code implementations NAACL (TeachingNLP) 2021 Emma Manning, Nathan Schneider, Amir Zeldes

This paper describes the primarily-graduate computational linguistics and NLP curriculum at Georgetown University, a U. S. university that has seen significant growth in these areas in recent years.

DocAMR: Multi-Sentence AMR Representation and Evaluation

no code implementations15 Dec 2021 Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O'Gorman, Young-suk Lee, Jeffrey Flanigan, Ramón Fernandez Astudillo, Radu Florian, Salim Roukos, Nathan Schneider

Despite extensive research on parsing of English sentences into Abstraction Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation.

Coreference Resolution

Oracle Linguistic Graphs Complement a Pretrained Transformer Language Model: A Cross-formalism Comparison

no code implementations15 Dec 2021 Jakob Prange, Nathan Schneider, Lingpeng Kong

We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling.

Language Modelling

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

1 code implementation COLING (LAW) 2020 Michael Kranzlein, Emma Manning, Siyao Peng, Shira Wein, Aryaman Arora, Bradford Salen, Nathan Schneider

We present the Prepositions Annotated with Supersense Tags in Reddit International English ("PASTRIE") corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish.

Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces with Pseudowords

no code implementations23 Sep 2021 Taelin Karidi, Yichu Zhou, Nathan Schneider, Omri Abend, Vivek Srikumar

We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses.

BERT Has Uncommon Sense: Similarity Ranking for Word Sense BERTology

1 code implementation EMNLP (BlackboxNLP) 2021 Luke Gessler, Nathan Schneider

An important question concerning contextualized word embedding (CWE) models like BERT is how well they can represent different word senses, especially those in the long tail of uncommon senses.

Making Heads and Tails of Models with Marginal Calibration for Sparse Tagsets

1 code implementation Findings (EMNLP) 2021 Michael Kranzlein, Nelson F. Liu, Nathan Schneider

For interpreting the behavior of a probabilistic model, it is useful to measure a model's calibration--the extent to which it produces reliable confidence scores.

TAG

Mischievous Nominal Constructions in Universal Dependencies

no code implementations UDW (SyntaxFest) 2021 Nathan Schneider, Amir Zeldes

While the highly multilingual Universal Dependencies (UD) project provides extensive guidelines for clausal structure as well as structure within canonical nominal phrases, a standard treatment is lacking for many "mischievous" nominal phenomena that break the mold.

Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments

1 code implementation ACL 2021 Austin Blodgett, Nathan Schneider

We present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences.

AMR Parsing

Hindi-Urdu Adposition and Case Supersenses v1.0

no code implementations2 Mar 2021 Aryaman Arora, Nitin Venkateswaran, Nathan Schneider

These are the guidelines for the application of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al. 2018) to Modern Standard Hindi of Delhi.

UCCA's Foundational Layer: Annotation Guidelines v2.1

1 code implementation31 Dec 2020 Omri Abend, Nathan Schneider, Dotan Dvir, Jakob Prange, Ari Rappoport

This is the annotation manual for Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013), specifically the Foundational Layer.

Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories

1 code implementation2 Dec 2020 Jakob Prange, Nathan Schneider, Vivek Srikumar

Our best tagger is capable of recovering a sizeable fraction of the long-tail supertags and even generates CCG categories that have never been seen in training, while approximating the prior state of the art in overall tag accuracy with fewer parameters.

Structured Prediction TAG

Cross-lingual Semantic Representation for NLP with UCCA

no code implementations COLING 2020 Omri Abend, Dotan Dvir, Daniel Hershcovich, Jakob Prange, Nathan Schneider

This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew.

UCCA Parsing

Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics

2 code implementations COLING 2020 Daniel Hershcovich, Nathan Schneider, Dotan Dvir, Jakob Prange, Miryam de Lhoneux, Omri Abend

Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other.

Natural Language Understanding

Lexical Semantic Recognition

2 code implementations ACL (MWE) 2021 Nelson F. Liu, Daniel Hershcovich, Michael Kranzlein, Nathan Schneider

In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence.

Sentence segmentation

Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in Hindi and Punjabi

1 code implementation ACL 2020 Aryaman Arora, Luke Gessler, Nathan Schneider

Hindi grapheme-to-phoneme (G2P) conversion is mostly trivial, with one exception: whether a schwa represented in the orthography is pronounced or unpronounced (deleted).

A Human Evaluation of AMR-to-English Generation Systems

no code implementations COLING 2020 Emma Manning, Shira Wein, Nathan Schneider

Most current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language generation.

Text Generation

A Corpus of Adpositional Supersenses for Mandarin Chinese

no code implementations LREC 2020 Siyao Peng, Yang Liu, YIlun Zhu, Austin Blodgett, Yushi Zhao, Nathan Schneider

Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language.

Translation

Made for Each Other: Broad-coverage Semantic Structures Meet Preposition Supersenses

1 code implementation CONLL 2019 Jakob Prange, Nathan Schneider, Omri Abend

Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013) is a typologically-informed, broad-coverage semantic annotation scheme that describes coarse-grained predicate-argument structure but currently lacks semantic roles.

Preparing SNACS for Subjects and Objects

1 code implementation WS 2019 Adi Shalev, Jena D. Hwang, Nathan Schneider, Vivek Srikumar, Omri Abend, Ari Rappoport

Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens.

Semantically Constrained Multilayer Annotation: The Case of Coreference

no code implementations WS 2019 Jakob Prange, Nathan Schneider, Omri Abend

We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions.

An Improved Approach for Semantic Graph Composition with CCG

no code implementations WS 2019 Austin Blodgett, Nathan Schneider

We define new semantics for the CCG combinators that is better suited to deriving AMR graphs.

AMR Parsing

Adpositional Supersenses for Mandarin Chinese

no code implementations6 Dec 2018 YIlun Zhu, Yang Liu, Siyao Peng, Austin Blodgett, Yushi Zhao, Nathan Schneider

This study adapts Semantic Network of Adposition and Case Supersenses (SNACS) annotation to Mandarin Chinese and demonstrates that the same supersense categories are appropriate for Chinese adposition semantics.

Machine Translation Translation

Annotation of Tense and Aspect Semantics for Sentential AMR

no code implementations COLING 2018 Lucia Donatelli, Michael Regan, William Croft, Nathan Schneider

Although English grammar encodes a number of semantic contrasts with tense and aspect marking, these semantics are currently ignored by Abstract Meaning Representation (AMR) annotations.

Entity Typing

Constructing an Annotated Corpus of Verbal MWEs for English

no code implementations COLING 2018 Abigail Walsh, Claire Bonial, Kristina Geeraert, John P. McCrae, Nathan Schneider, Clarissa Somers

This paper describes the construction and annotation of a corpus of verbal MWEs for English, as part of the PARSEME Shared Task 1. 1 on automatic identification of verbal MWEs.

Word Alignment

Leaving no token behind: comprehensive (and delicious) annotation of MWEs and supersenses

no code implementations COLING 2018 Nathan Schneider

I will describe an unorthodox approach to lexical semantic annotation that prioritizes corpus coverage, democratizing analysis of a wide range of expression types.

Discourse Coherence: Concurrent Explicit and Implicit Relations

no code implementations ACL 2018 Hannah Rohde, Alex Johnson, er, Nathan Schneider, Bonnie Webber

Theories of discourse coherence posit relations between discourse segments as a key feature of coherent text.

Discourse Parsing

A Structured Syntax-Semantics Interface for English-AMR Alignment

1 code implementation NAACL 2018 Ida Szubert, Adam Lopez, Nathan Schneider

Abstract Meaning Representation (AMR) annotations are often assumed to closely mirror dependency syntax, but AMR explicitly does not require this, and the assumption has never been tested.

AMR Parsing

Comprehensive Supersense Disambiguation of English Prepositions and Possessives

1 code implementation ACL 2018 Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend

Semantic relations are often signaled with prepositional or possessive marking--but extreme polysemy bedevils their analysis and automatic interpretation.

Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions

no code implementations SEMEVAL 2017 Jena D. Hwang, Archna Bhatia, Na-Rae Han, Tim O{'}Gorman, Vivek Srikumar, Nathan Schneider

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4, 250 preposition tokens in a 55, 000 word corpus of English.

Adposition and Case Supersenses v2.5: Guidelines for English

2 code implementations7 Apr 2017 Nathan Schneider, Jena D. Hwang, Archna Bhatia, Vivek Srikumar, Na-Rae Han, Tim O'Gorman, Sarah R. Moeller, Omri Abend, Adi Shalev, Austin Blodgett, Jakob Prange

This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 50 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github. com/nert-gu/streusle/; version 4. 3 tracks guidelines version 2. 5).

The NLTK FrameNet API: Designing for Discoverability with a Rich Linguistic Resource

no code implementations EMNLP 2017 Nathan Schneider, Chuck Wooters

A new Python API, integrated within the NLTK suite, offers access to the FrameNet 1. 7 lexical database.

Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions

no code implementations10 Mar 2017 Jena D. Hwang, Archna Bhatia, Na-Rae Han, Tim O'Gorman, Vivek Srikumar, Nathan Schneider

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4, 250 preposition tokens in a 55, 000 word corpus of English.

A corpus of preposition supersenses in English web reviews

no code implementations8 May 2016 Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Meredith Green, Kathryn Conger, Tim O'Gorman, Martha Palmer

We present the first corpus annotated with preposition supersenses, unlexicalized categories for semantic functions that can be marked by English prepositions (Schneider et al., 2015).

Inconsistency Detection in Semantic Annotation

1 code implementation LREC 2016 Nora Hollenstein, Nathan Schneider, Bonnie Webber

Automatically finding these inconsistencies and correcting them (even manually) can increase the quality of the data.

Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks

no code implementations21 Apr 2015 Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Nathan Schneider, Timothy Baldwin

Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing applications.

Chunking NER +3

Augmenting English Adjective Senses with Supersenses

1 code implementation LREC 2014 Yulia Tsvetkov, Nathan Schneider, Dirk Hovy, Archna Bhatia, Manaal Faruqui, Chris Dyer

We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification.

General Classification

Comprehensive Annotation of Multiword Expressions in a Social Web Corpus

no code implementations LREC 2014 Nathan Schneider, Spencer Onuffer, Nora Kazour, Emily Danchik, Michael T. Mordowanec, Henrietta Conrad, Noah A. Smith

Multiword expressions (MWEs) are quite frequent in languages such as English, but their diversity, the scarcity of individual MWE types, and contextual ambiguity have presented obstacles to corpus-based studies and NLP systems addressing them as a class.

Language Acquisition Machine Translation

Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut

no code implementations TACL 2014 Nathan Schneider, Emily Danchik, Chris Dyer, Noah A. Smith

We present a novel representation, evaluation measure, and supervised models for the task of identifying the multiword expressions (MWEs) in a sentence, resulting in a lexical semantic segmentation.

Chunking Semantic Segmentation

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