Katrin Erk


2020

pdf bib
When is a bishop not like a rook? When it’s like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships
Gabriella Chronis | Katrin Erk
Proceedings of the 24th Conference on Computational Natural Language Learning

This paper investigates contextual language models, which produce token representations, as a resource for lexical semantics at the word or type level. We construct multi-prototype word embeddings from bert-base-uncased (Devlin et al., 2018). These embeddings retain contextual knowledge that is critical for some type-level tasks, while being less cumbersome and less subject to outlier effects than exemplar models. Similarity and relatedness estimation, both type-level tasks, benefit from this contextual knowledge, indicating the context-sensitivity of these processes. BERT’s token level knowledge also allows the testing of a type-level hypothesis about lexical abstractness, demonstrating the relationship between token-level phenomena and type-level concreteness ratings. Our findings provide important insight into the interpretability of BERT: layer 7 approximates semantic similarity, while the final layer (11) approximates relatedness.

pdf bib
Leveraging WordNet Paths for Neural Hypernym Prediction
Yejin Cho | Juan Diego Rodriguez | Yifan Gao | Katrin Erk
Proceedings of the 28th International Conference on Computational Linguistics

We formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet. Our experiments with encoder-decoder models show that training to generate taxonomy paths can improve the performance of direct hypernym prediction. As a simple but powerful model, the hypo2path model achieves state-of-the-art performance, outperforming the best benchmark by 4.11 points in hit-at-one (H@1).

pdf bib
Help! Need Advice on Identifying Advice
Venkata Subrahmanyan Govindarajan | Benjamin Chen | Rebecca Warholic | Katrin Erk | Junyi Jessy Li
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of advice-seeking online, as well as advice-giving in natural language generation systems. We present a dataset in English from two Reddit advice forums - r/AskParents and r/needadvice - annotated for whether sentences in posts contain advice or not. Our analysis reveals rich linguistic phenomena in advice discourse. We present preliminary models showing that while pre-trained language models are able to capture advice better than rule-based systems, advice identification is challenging, and we identify directions for future research.

2019

pdf bib
Query-focused Scenario Construction
Su Wang | Greg Durrett | Katrin Erk
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The news coverage of events often contains not one but multiple incompatible accounts of what happened. We develop a query-based system that extracts compatible sets of events (scenarios) from such data, formulated as one-class clustering. Our system incrementally evaluates each event’s compatibility with already selected events, taking order into account. We use synthetic data consisting of article mixtures for scalable training and evaluate our model on a new human-curated dataset of scenarios about real-world news topics. Stronger neural network models and harder synthetic training settings are both important to achieve high performance, and our final scenario construction system substantially outperforms baselines based on prior work.

pdf bib
Evaluating Discourse in Structured Text Representations
Elisa Ferracane | Greg Durrett | Junyi Jessy Li | Katrin Erk
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Discourse structure is integral to understanding a text and is helpful in many NLP tasks. Learning latent representations of discourse is an attractive alternative to acquiring expensive labeled discourse data. Liu and Lapata (2018) propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. We examine this model in detail, and evaluate on additional discourse-relevant tasks and datasets, in order to assess whether the structured attention improves performance on the end task and whether it captures a text’s discourse structure. We find the learned latent trees have little to no structure and instead focus on lexical cues; even after obtaining more structured trees with proposed model modifications, the trees are still far from capturing discourse structure when compared to discourse dependency trees from an existing discourse parser. Finally, ablation studies show the structured attention provides little benefit, sometimes even hurting performance.

pdf bib
From News to Medical: Cross-domain Discourse Segmentation
Elisa Ferracane | Titan Page | Junyi Jessy Li | Katrin Erk
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

The first step in discourse analysis involves dividing a text into segments. We annotate the first high-quality small-scale medical corpus in English with discourse segments and analyze how well news-trained segmenters perform on this domain. While we expectedly find a drop in performance, the nature of the segmentation errors suggests some problems can be addressed earlier in the pipeline, while others would require expanding the corpus to a trainable size to learn the nuances of the medical domain.

2018

pdf bib
Deep Neural Models of Semantic Shift
Alex Rosenfeld | Katrin Erk
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Diachronic distributional models track changes in word use over time. In this paper, we propose a deep neural network diachronic distributional model. Instead of modeling lexical change via a time series as is done in previous work, we represent time as a continuous variable and model a word’s usage as a function of time. Additionally, we have also created a novel synthetic task which measures a model’s ability to capture the semantic trajectory. This evaluation quantitatively measures how well a model captures the semantic trajectory of a word over time. Finally, we explore how well the derivatives of our model can be used to measure the speed of lexical change.

pdf bib
Implicit Argument Prediction with Event Knowledge
Pengxiang Cheng | Katrin Erk
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be generated automatically at scale. This allows us to use a neural model, which draws on narrative coherence and entity salience for predictions. We show that our model has superior performance on both synthetic and natural data.

pdf bib
Modeling Semantic Plausibility by Injecting World Knowledge
Su Wang | Greg Durrett | Katrin Erk
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested. However both are physically plausible events. This paper introduces the task of semantic plausibility: recognizing plausible but possibly novel events. We present a new crowdsourced dataset of semantic plausibility judgments of single events such as man swallow paintball. Simple models based on distributional representations perform poorly on this task, despite doing well on selection preference, but injecting manually elicited knowledge about entity properties provides a substantial performance boost. Our error analysis shows that our new dataset is a great testbed for semantic plausibility models: more sophisticated knowledge representation and propagation could address many of the remaining errors.

pdf bib
Picking Apart Story Salads
Su Wang | Eric Holgate | Greg Durrett | Katrin Erk
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

During natural disasters and conflicts, information about what happened is often confusing and messy, and distributed across many sources. We would like to be able to automatically identify relevant information and assemble it into coherent narratives of what happened. To make this task accessible to neural models, we introduce Story Salads, mixtures of multiple documents that can be generated at scale. By exploiting the Wikipedia hierarchy, we can generate salads that exhibit challenging inference problems. Story salads give rise to a novel, challenging clustering task, where the objective is to group sentences from the same narratives. We demonstrate that simple bag-of-words similarity clustering falls short on this task, and that it is necessary to take into account global context and coherence.

2017

pdf bib
Distributional Modeling on a Diet: One-shot Word Learning from Text Only
Su Wang | Stephen Roller | Katrin Erk
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative.

2016

pdf bib
Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment
Stephen Roller | Katrin Erk
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
PIC a Different Word: A Simple Model for Lexical Substitution in Context
Stephen Roller | Katrin Erk
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Katrin Erk | Noah A. Smith
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Katrin Erk | Noah A. Smith
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

pdf bib
Leveraging coreference to identify arms in medical abstracts: An experimental study
Elisa Ferracane | Iain Marshall | Byron C. Wallace | Katrin Erk
Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

pdf bib
Word Sense Clustering and Clusterability
Diana McCarthy | Marianna Apidianaki | Katrin Erk
Computational Linguistics, Volume 42, Issue 2 - June 2016

pdf bib
Representing Meaning with a Combination of Logical and Distributional Models
I. Beltagy | Stephen Roller | Pengxiang Cheng | Katrin Erk | Raymond J. Mooney
Computational Linguistics, Volume 42, Issue 4 - December 2016

2015

pdf bib
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics
Islam Beltagy | Katrin Erk
Proceedings of the 11th International Conference on Computational Semantics

2014

pdf bib
UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic
Islam Beltagy | Stephen Roller | Gemma Boleda | Katrin Erk | Raymond Mooney
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

pdf bib
Probabilistic Soft Logic for Semantic Textual Similarity
Islam Beltagy | Katrin Erk | Raymond Mooney
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
What Substitutes Tell Us - Analysis of an “All-Words” Lexical Substitution Corpus
Gerhard Kremer | Katrin Erk | Sebastian Padó | Stefan Thater
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

pdf bib
Semantic Parsing using Distributional Semantics and Probabilistic Logic
Islam Beltagy | Katrin Erk | Raymond Mooney
Proceedings of the ACL 2014 Workshop on Semantic Parsing

pdf bib
Who Evoked that Frame? Some Thoughts on Context Effects and Event Types
Katrin Erk
Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014)

pdf bib
Inclusive yet Selective: Supervised Distributional Hypernymy Detection
Stephen Roller | Katrin Erk | Gemma Boleda
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

bib
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers
Alexander Koller | Katrin Erk
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

pdf bib
Towards a semantics for distributional representations
Katrin Erk
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

bib
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Short Papers
Alexander Koller | Katrin Erk
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Short Papers

pdf bib
Measuring Word Meaning in Context
Katrin Erk | Diana McCarthy | Nicholas Gaylord
Computational Linguistics, Volume 39, Issue 3 - September 2013

pdf bib
NAACL HLT 2013 Tutorial Abstracts
Jimmy Lin | Katrin Erk
NAACL HLT 2013 Tutorial Abstracts

pdf bib
Montague Meets Markov: Deep Semantics with Probabilistic Logical Form
Islam Beltagy | Cuong Chau | Gemma Boleda | Dan Garrette | Katrin Erk | Raymond Mooney
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2011

pdf bib
Integrating Logical Representations with Probabilistic Information using Markov Logic
Dan Garrette | Katrin Erk | Raymond Mooney
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

pdf bib
Simple Unsupervised Grammar Induction from Raw Text with Cascaded Finite State Models
Elias Ponvert | Jason Baldridge | Katrin Erk
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

pdf bib
A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences
Katrin Erk | Sebastian Padó | Ulrike Padó
Computational Linguistics, Volume 36, Issue 4 - December 2010

pdf bib
What Is Word Meaning, Really? (And How Can Distributional Models Help Us Describe It?)
Katrin Erk
Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics

pdf bib
Exemplar-Based Models for Word Meaning in Context
Katrin Erk | Sebastian Padó
Proceedings of the ACL 2010 Conference Short Papers

pdf bib
Proceedings of the 5th International Workshop on Semantic Evaluation
Katrin Erk | Carlo Strapparava
Proceedings of the 5th International Workshop on Semantic Evaluation

pdf bib
Crouching Dirichlet, Hidden Markov Model: Unsupervised POS Tagging with Context Local Tag Generation
Taesun Moon | Katrin Erk | Jason Baldridge
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

pdf bib
Graded Word Sense Assignment
Katrin Erk | Diana McCarthy
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

pdf bib
Unsupervised morphological segmentation and clustering with document boundaries
Taesun Moon | Katrin Erk | Jason Baldridge
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

pdf bib
Paraphrase Assessment in Structured Vector Space: Exploring Parameters and Datasets
Katrin Erk | Sebastian Padó
Proceedings of the Workshop on Geometrical Models of Natural Language Semantics

pdf bib
Representing words as regions in vector space
Katrin Erk
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

pdf bib
Measuring semantic relatedness with vector space models and random walks
Amaç Herdağdelen | Katrin Erk | Marco Baroni
Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4)

pdf bib
Supporting inferences in semantic space: representing words as regions
Katrin Erk
Proceedings of the Eight International Conference on Computational Semantics

pdf bib
Investigations on Word Senses and Word Usages
Katrin Erk | Diana McCarthy | Nicholas Gaylord
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

pdf bib
Teaching Computational Linguistics to a Large, Diverse Student Body: Courses, Tools, and Interdepartmental Interaction
Jason Baldridge | Katrin Erk
Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics

pdf bib
A Structured Vector Space Model for Word Meaning in Context
Katrin Erk | Sebastian Padó
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

pdf bib
IGT-XML: An XML Format for Interlinearized Glossed Text
Alexis Palmer | Katrin Erk
Proceedings of the Linguistic Annotation Workshop

pdf bib
SemEval-2007 Task 19: Frame Semantic Structure Extraction
Collin Baker | Michael Ellsworth | Katrin Erk
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

pdf bib
A Simple, Similarity-based Model for Selectional Preferences
Katrin Erk
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

pdf bib
Flexible, Corpus-Based Modelling of Human Plausibility Judgements
Sebastian Padó | Ulrike Padó | Katrin Erk
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

pdf bib
The SALSA Corpus: a German Corpus Resource for Lexical Semantics
Aljoscha Burchardt | Katrin Erk | Anette Frank | Andrea Kowalski | Sebastian Padó | Manfred Pinkal
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper describes the SALSA corpus, a large German corpus manually annotated with manual role-semantic annotation, based on the syntactically annotated TIGER newspaper corpus. The first release, comprising about 20,000 annotated predicate instances (about half the TIGER corpus), is scheduled for mid-2006. In this paper we discuss the annotation framework (frame semantics) and its cross-lingual applicability, problems arising from exhaustive annotation, strategies for quality control, and possible applications.

pdf bib
SALTO - A Versatile Multi-Level Annotation Tool
Aljoscha Burchardt | Katrin Erk | Anette Frank | Andrea Kowalski | Sebastian Pado
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper, we describe the SALTO tool. It was originally developed for the annotation of semantic roles in the frame semantics paradigm, but can be used for graphical annotation of treebanks with general relational information in a simple drag-and-drop fashion. The tool additionally supports corpus management and quality control.

pdf bib
Shalmaneser - A Toolchain For Shallow Semantic Parsing
Katrin Erk | Sebastian Padó
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper presents Shalmaneser, a software package for shallow semantic parsing, the automatic assignment of semantic classes and roles to free text. Shalmaneser is a toolchain of independent modules communicating through a common XML format. System output can be inspected graphically. Shalmaneser can be used either as a “black box” to obtain semantic parses for new datasets (classifiers for English and German frame-semantic analysis are included), or as a research platform that can be extended to new parsers, languages, or classification paradigms.

pdf bib
Unknown word sense detection as outlier detection
Katrin Erk
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2005

pdf bib
Analyzing Models for Semantic Role Assignment using Confusability
Katrin Erk | Sebastian Padó
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

pdf bib
A Powerful and Versatile XML Format for Representing Role-semantic Annotation
Katrin Erk | Sebastian Padó
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

pdf bib
Querying Both Time-aligned and Hierarchical Corpora with NXT Search
Ulrich Heid | Holger Voormann | Jan-Torsten Milde | Ulrike Gut | Katrin Erk | Sebastian Padó
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

pdf bib
Semantic role labelling with similarity-based generalization using EM-based clustering
Ulrike Baldewein | Katrin Erk | Sebastian Padó | Detlef Prescher
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

pdf bib
Semantic Role Labelling With Chunk Sequences
Ulrike Baldewein | Katrin Erk | Sebastian Padó | Detlef Prescher
Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004

2003

pdf bib
Well-Nested Parallelism Constraints for Ellipsis Resolution
Katrin Erk | Joachim Niehren
10th Conference of the European Chapter of the Association for Computational Linguistics

pdf bib
Towards a Resource for Lexical Semantics: A Large German Corpus with Extensive Semantic Annotation
Katrin Erk | Andrea Kowalski | Sebastian Padó | Manfred Pinkal
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

2001

pdf bib
Underspecified Beta Reduction
Manuel Bodirsky | Katrin Erk | Alexander Koller | Joachim Niehren
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics