Sabine Schulte im Walde

Also published as: Sabine Schulte Im Walde, Sabine Schulte in Walde


2019

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SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction
Anna Hätty | Dominik Schlechtweg | Sabine Schulte im Walde
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

We introduce SURel, a novel dataset with human-annotated meaning shifts between general-language and domain-specific contexts. We show that meaning shifts of term candidates cause errors in term extraction, and demonstrate that the SURel annotation reflects these errors. Furthermore, we illustrate that SURel enables us to assess optimisations of term extraction techniques when incorporating meaning shifts.

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A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains
Dominik Schlechtweg | Anna Hätty | Marco Del Tredici | Sabine Schulte im Walde
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and semantic sense changes across domains. Our work addresses the superficialness and lack of comparison in assessing models of diachronic lexical change, by bringing together and extending benchmark models on a common state-of-the-art evaluation task. In addition, we demonstrate that the same evaluation task and modelling approaches can successfully be utilised for the synchronic detection of domain-specific sense divergences in the field of term extraction.

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You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP
Marco Del Tredici | Diego Marcheggiani | Sabine Schulte im Walde | Raquel Fernández
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Information about individuals can help to better understand what they say, particularly in social media where texts are short. Current approaches to modelling social media users pay attention to their social connections, but exploit this information in a static way, treating all connections uniformly. This ignores the fact, well known in sociolinguistics, that an individual may be part of several communities which are not equally relevant in all communicative situations. We present a model based on Graph Attention Networks that captures this observation. It dynamically explores the social graph of a user, computes a user representation given the most relevant connections for a target task, and combines it with linguistic information to make a prediction. We apply our model to three different tasks, evaluate it against alternative models, and analyse the results extensively, showing that it significantly outperforms other current methods.

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Distributional Interaction of Concreteness and Abstractness in Verb–Noun Subcategorisation
Diego Frassinelli | Sabine Schulte im Walde
Proceedings of the 13th International Conference on Computational Semantics - Short Papers

In recent years, both cognitive and computational research has provided empirical analyses of contextual co-occurrence of concrete and abstract words, partially resulting in inconsistent pictures. In this work we provide a more fine-grained description of the distributional nature in the corpus- based interaction of verbs and nouns within subcategorisation, by investigating the concreteness of verbs and nouns that are in a specific syntactic relationship with each other, i.e., subject, direct object, and prepositional object. Overall, our experiments show consistent patterns in the distributional representation of subcategorising and subcategorised concrete and abstract words. At the same time, the studies reveal empirical evidence why contextual abstractness represents a valuable indicator for automatic non-literal language identification.

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Second-order Co-occurrence Sensitivity of Skip-Gram with Negative Sampling
Dominik Schlechtweg | Cennet Oguz | Sabine Schulte im Walde
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

We simulate first- and second-order context overlap and show that Skip-Gram with Negative Sampling is similar to Singular Value Decomposition in capturing second-order co-occurrence information, while Pointwise Mutual Information is agnostic to it. We support the results with an empirical study finding that the models react differently when provided with additional second-order information. Our findings reveal a basic property of Skip-Gram with Negative Sampling and point towards an explanation of its success on a variety of tasks.

2018

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Assessing Meaning Components in German Complex Verbs: A Collection of Source-Target Domains and Directionality
Sabine Schulte im Walde | Maximilian Köper | Sylvia Springorum
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

This paper presents a collection to assess meaning components in German complex verbs, which frequently undergo meaning shifts. We use a novel strategy to obtain source and target domain characterisations via sentence generation rather than sentence annotation. A selection of arrows adds spatial directional information to the generated contexts. We provide a broad qualitative description of the dataset, and a series of standard classification experiments verifies the quantitative reliability of the presented resource. The setup for collecting the meaning components is applicable also to other languages, regarding complex verbs as well as other language-specific targets that involve meaning shifts.

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Quantitative Semantic Variation in the Contexts of Concrete and Abstract Words
Daniela Naumann | Diego Frassinelli | Sabine Schulte im Walde
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Across disciplines, researchers are eager to gain insight into empirical features of abstract vs. concrete concepts. In this work, we provide a detailed characterisation of the distributional nature of abstract and concrete words across 16,620 English nouns, verbs and adjectives. Specifically, we investigate the following questions: (1) What is the distribution of concreteness in the contexts of concrete and abstract target words? (2) What are the differences between concrete and abstract words in terms of contextual semantic diversity? (3) How does the entropy of concrete and abstract word contexts differ? Overall, our studies show consistent differences in the distributional representation of concrete and abstract words, thus challenging existing theories of cognition and providing a more fine-grained description of their nature.

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Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains
Jeremy Barnes | Roman Klinger | Sabine Schulte im Walde
Proceedings of the 27th International Conference on Computational Linguistics

Domain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to this problem are structural correspondence learning and autoencoders. However, they either require long training times or suffer greatly on highly divergent domains. Inspired by recent advances in cross-lingual sentiment analysis, we provide a novel perspective and cast the domain adaptation problem as an embedding projection task. Our model takes as input two mono-domain embedding spaces and learns to project them to a bi-domain space, which is jointly optimized to (1) project across domains and to (2) predict sentiment. We perform domain adaptation experiments on 20 source-target domain pairs for sentiment classification and report novel state-of-the-art results on 11 domain pairs, including the Amazon domain adaptation datasets and SemEval 2013 and 2016 datasets. Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains. Our code is available at https://github.com/jbarnesspain/domain_blse

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Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution
Ina Roesiger | Maximilian Köper | Kim Anh Nguyen | Sabine Schulte im Walde
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

Cases of coreference and bridging resolution often require knowledge about semantic relations between anaphors and antecedents. We suggest state-of-the-art neural-network classifiers trained on relation benchmarks to predict and integrate likelihoods for relations. Two experiments with representations differing in noise and complexity improve our bridging but not our coreference resolver.

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Fine-Grained Termhood Prediction for German Compound Terms Using Neural Networks
Anna Hätty | Sabine Schulte im Walde
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

Automatic term identification and investigating the understandability of terms in a specialized domain are often treated as two separate lines of research. We propose a combined approach for this matter, by defining fine-grained classes of termhood and framing a classification task. The classes reflect tiers of a term’s association to a domain. The new setup is applied to German closed compounds as term candidates in the domain of cooking. For the prediction of the classes, we compare several neural network architectures and also take salient information about the compounds’ components into account. We show that applying a similar class distinction to the compounds’ components and propagating this information within the network improves the compound class prediction results.

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Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
Jeremy Barnes | Roman Klinger | Sabine Schulte im Walde
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches. However, they either require large amounts of parallel data or do not sufficiently capture sentiment information. We introduce Bilingual Sentiment Embeddings (BLSE), which jointly represent sentiment information in a source and target language. This model only requires a small bilingual lexicon, a source-language corpus annotated for sentiment, and monolingual word embeddings for each language. We perform experiments on three language combinations (Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment classification and find that our model significantly outperforms state-of-the-art methods on four out of six experimental setups, as well as capturing complementary information to machine translation. Our analysis of the resulting embedding space provides evidence that it represents sentiment information in the resource-poor target language without any annotated data in that language.

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Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models
Maximilian Köper | Sabine Schulte im Walde
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs. In contrast to corpus-based studies, a novel dataset demonstrates that “regular” shifts represent the smallest class. Classification experiments relying on a standard similarity model successfully distinguish between four types of shifts, with verb classes boosting the performance, and affective features for abstractness, emotion and sentiment representing the most salient indicators.

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Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change
Dominik Schlechtweg | Sabine Schulte im Walde | Stefanie Eckmann
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning, to counteract the lack of resources for evaluating computational models of lexical semantic change. Our framework exploits an intuitive notion of semantic relatedness, and distinguishes between innovative and reductive meaning changes with high inter-annotator agreement. The resulting test set for German comprises ratings from five annotators for the relatedness of 1,320 use pairs across 22 target words.

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Introducing Two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness
Kim Anh Nguyen | Sabine Schulte im Walde | Ngoc Thang Vu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We present two novel datasets for the low-resource language Vietnamese to assess models of semantic similarity: ViCon comprises pairs of synonyms and antonyms across word classes, thus offering data to distinguish between similarity and dissimilarity. ViSim-400 provides degrees of similarity across five semantic relations, as rated by human judges. The two datasets are verified through standard co-occurrence and neural network models, showing results comparable to the respective English datasets.

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A Laypeople Study on Terminology Identification across Domains and Task Definitions
Anna Hätty | Sabine Schulte im Walde
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

This paper introduces a new dataset of term annotation. Given that even experts vary significantly in their understanding of termhood, and that term identification is mostly performed as a binary task, we offer a novel perspective to explore the common, natural understanding of what constitutes a term: Laypeople annotate single-word and multi-word terms, across four domains and across four task definitions. Analyses based on inter-annotator agreement offer insights into differences in term specificity, term granularity and subtermhood.

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Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs
Eleri Aedmaa | Maximilian Köper | Sabine Schulte im Walde
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

This paper presents two novel datasets and a random-forest classifier to automatically predict literal vs. non-literal language usage for a highly frequent type of multi-word expression in a low-resource language, i.e., Estonian. We demonstrate the value of language-specific indicators induced from theoretical linguistic research, which outperform a high majority baseline when combined with language-independent features of non-literal language (such as abstractness).

2017

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Factoring Ambiguity out of the Prediction of Compositionality for German Multi-Word Expressions
Stefan Bott | Sabine Schulte im Walde
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

Ambiguity represents an obstacle for distributional semantic models(DSMs), which typically subsume the contexts of all word senses within one vector. While individual vector space approaches have been concerned with sense discrimination (e.g., Schütze 1998, Erk 2009, Erk and Pado 2010), such discrimination has rarely been integrated into DSMs across semantic tasks. This paper presents a soft-clustering approach to sense discrimination that filters sense-irrelevant features when predicting the degrees of compositionality for German noun-noun compounds and German particle verbs.

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Complex Verbs are Different: Exploring the Visual Modality in Multi-Modal Models to Predict Compositionality
Maximilian Köper | Sabine Schulte im Walde
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

This paper compares a neural network DSM relying on textual co-occurrences with a multi-modal model integrating visual information. We focus on nominal vs. verbal compounds, and zoom into lexical, empirical and perceptual target properties to explore the contribution of the visual modality. Our experiments show that (i) visual features contribute differently for verbs than for nouns, and (ii) images complement textual information, if (a) the textual modality by itself is poor and appropriate image subsets are used, or (b) the textual modality by itself is rich and large (potentially noisy) images are added.

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Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses
Maximilian Köper | Sabine Schulte im Walde
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications

Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for larger units by propagating abstractness to verb-noun pairs which lead to better metaphor detection iii) we overcome the limitation of learning a single rating per word and show that multi-sense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3million English words and multi-words as well as automatically created sense specific abstractness ratings

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Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
Jeremy Barnes | Roman Klinger | Sabine Schulte im Walde
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets. In some papers, however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accordingly, it is hard to understand how well a certain model generalizes across different tasks and datasets. In this paper, we contribute to this situation by comparing several models on six different benchmarks, which belong to different domains and additionally have different levels of granularity (binary, 3-class, 4-class and 5-class). We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (i.e., with more than two classes). Incorporating sentiment information into word embeddings during training gives good results for datasets that are lexically similar to the training data. With our experiments, we contribute to a better understanding of the performance of different model architectures on different data sets. Consequently, we detect novel state-of-the-art results on the SenTube datasets.

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Exploring Soft-Clustering for German (Particle) Verbs across Frequency Ranges
Moritz Wittmann | Maximilian Köper | Sabine Schulte im Walde
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

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Exploring Multi-Modal Text+Image Models to Distinguish between Abstract and Concrete Nouns
Sai Abishek Bhaskar | Maximilian Köper | Sabine Schulte Im Walde | Diego Frassinelli
Proceedings of the IWCS workshop on Foundations of Situated and Multimodal Communication

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Hierarchical Embeddings for Hypernymy Detection and Directionality
Kim Anh Nguyen | Maximilian Köper | Sabine Schulte im Walde | Ngoc Thang Vu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym–hyponym distributional hierarchy. Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages. Results on benchmark datasets show that HyperVec outperforms both state-of-the-art unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.

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Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
Kim Anh Nguyen | Sabine Schulte im Walde | Ngoc Thang Vu
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees. In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. The results from classification experiments show that AntSynNET improves the performance over prior pattern-based methods.

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Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language
Maximilian Köper | Sabine Schulte im Walde
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Up to date, the majority of computational models still determines the semantic relatedness between words (or larger linguistic units) on the type level. In this paper, we compare and extend multi-sense embeddings, in order to model and utilise word senses on the token level. We focus on the challenging class of complex verbs, and evaluate the model variants on various semantic tasks: semantic classification; predicting compositionality; and detecting non-literal language usage. While there is no overall best model, all models significantly outperform a word2vec single-sense skip baseline, thus demonstrating the need to distinguish between word senses in a distributional semantic model.

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Addressing Problems across Linguistic Levels in SMT: Combining Approaches to Model Morphology, Syntax and Lexical Choice
Marion Weller-Di Marco | Alexander Fraser | Sabine Schulte im Walde
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Many errors in phrase-based SMT can be attributed to problems on three linguistic levels: morphological complexity in the target language, structural differences and lexical choice. We explore combinations of linguistically motivated approaches to address these problems in English-to-German SMT and show that they are complementary to one another, but also that the popular verbal pre-ordering can cause problems on the morphological and lexical level. A discriminative classifier can overcome these problems, in particular when enriching standard lexical features with features geared towards verbal inflection.

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Evaluating the Reliability and Interaction of Recursively Used Feature Classes for Terminology Extraction
Anna Hätty | Michael Dorna | Sabine Schulte im Walde
Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics

Feature design and selection is a crucial aspect when treating terminology extraction as a machine learning classification problem. We designed feature classes which characterize different properties of terms based on distributions, and propose a new feature class for components of term candidates. By using random forests, we infer optimal features which are later used to build decision tree classifiers. We evaluate our method using the ACL RD-TEC dataset. We demonstrate the importance of the novel feature class for downgrading termhood which exploits properties of term components. Furthermore, our classification suggests that the identification of reliable term candidates should be performed successively, rather than just once.

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German in Flux: Detecting Metaphoric Change via Word Entropy
Dominik Schlechtweg | Stefanie Eckmann | Enrico Santus | Sabine Schulte im Walde | Daniel Hole
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change. We build the first diachronic test set for German as a standard for metaphoric change annotation. Our model is unsupervised, language-independent and generalizable to other processes of semantic change.

2016

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Automatic Semantic Classification of German Preposition Types: Comparing Hard and Soft Clustering Approaches across Features
Maximilian Köper | Sabine Schulte im Walde
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction
Kim Anh Nguyen | Sabine Schulte im Walde | Ngoc Thang Vu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Distinguishing Literal and Non-Literal Usage of German Particle Verbs
Maximilian Köper | Sabine Schulte im Walde
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Graph-based Clustering of Synonym Senses for German Particle Verbs
Moritz Wittmann | Marion Weller-Di Marco | Sabine Schulte im Walde
Proceedings of the 12th Workshop on Multiword Expressions

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Modeling Complement Types in Phrase-Based SMT
Marion Weller-Di Marco | Alexander Fraser | Sabine Schulte im Walde
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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GhoSt-PV: A Representative Gold Standard of German Particle Verbs
Stefan Bott | Nana Khvtisavrishvili | Max Kisselew | Sabine Schulte im Walde
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

German particle verbs represent a frequent type of multi-word-expression that forms a highly productive paradigm in the lexicon. Similarly to other multi-word expressions, particle verbs exhibit various levels of compositionality. One of the major obstacles for the study of compositionality is the lack of representative gold standards of human ratings. In order to address this bottleneck, this paper presents such a gold standard data set containing 400 randomly selected German particle verbs. It is balanced across several particle types and three frequency bands, and accomplished by human ratings on the degree of semantic compositionality.

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Improving Zero-Shot-Learning for German Particle Verbs by using Training-Space Restrictions and Local Scaling
Maximilian Köper | Sabine Schulte im Walde | Max Kisselew | Sebastian Padó
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

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The Role of Modifier and Head Properties in Predicting the Compositionality of English and German Noun-Noun Compounds: A Vector-Space Perspective
Sabine Schulte im Walde | Anna Hätty | Stefan Bott
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

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Neural-based Noise Filtering from Word Embeddings
Kim Anh Nguyen | Sabine Schulte im Walde | Ngoc Thang Vu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Word embeddings have been demonstrated to benefit NLP tasks impressively. Yet, there is room for improvements in the vector representations, because current word embeddings typically contain unnecessary information, i.e., noise. We propose two novel models to improve word embeddings by unsupervised learning, in order to yield word denoising embeddings. The word denoising embeddings are obtained by strengthening salient information and weakening noise in the original word embeddings, based on a deep feed-forward neural network filter. Results from benchmark tasks show that the filtered word denoising embeddings outperform the original word embeddings.

2015

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Exploiting Fine-grained Syntactic Transfer Features to Predict the Compositionality of German Particle Verbs
Stefan Bott | Sabine Schulte im Walde
Proceedings of the 11th International Conference on Computational Semantics

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Multilingual Reliability and “Semantic” Structure of Continuous Word Spaces
Maximilian Köper | Christian Scheible | Sabine Schulte im Walde
Proceedings of the 11th International Conference on Computational Semantics

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How to Account for Idiomatic German Support Verb Constructions in Statistical Machine Translation
Fabienne Cap | Manju Nirmal | Marion Weller | Sabine Schulte im Walde
Proceedings of the 11th Workshop on Multiword Expressions

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Predicting Prepositions for SMT
Marion Weller | Alexander Fraser | Sabine Schulte im Walde
Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Target-Side Generation of Prepositions for SMT
Marion Weller | Alexander Fraser | Sabine Schulte im Walde
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

2014

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Chasing Hypernyms in Vector Spaces with Entropy
Enrico Santus | Alessandro Lenci | Qin Lu | Sabine Schulte im Walde
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification
Michael Roth | Sabine Schulte im Walde
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Automatic Extraction of Synonyms for German Particle Verbs from Parallel Data with Distributional Similarity as a Re-Ranking Feature
Moritz Wittmann | Marion Weller | Sabine Schulte im Walde
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

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A Rank-based Distance Measure to Detect Polysemy and to Determine Salient Vector-Space Features for German Prepositions
Maximilian Köper | Sabine Schulte im Walde
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

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Fuzzy V-Measure - An Evaluation Method for Cluster Analyses of Ambiguous Data
Jason Utt | Sylvia Springorum | Maximilian Köper | Sabine Schulte im Walde
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

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Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality
Stefan Bott | Sabine Schulte im Walde
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

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Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models
Gabriella Lapesa | Stefan Evert | Sabine Schulte im Walde
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

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Syntactic Transfer Patterns of German Particle Verbs and their Impact on Lexical Semantics
Stefan Bott | Sabine Schulte im Walde
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

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Feature Norms of German Noun Compounds
Stephen Roller | Sabine Schulte im Walde
Proceedings of the 10th Workshop on Multiword Expressions (MWE)

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Modelling Regular Subcategorization Changes in German Particle Verbs
Stefan Bott | Sabine Schulte im Walde
Proceedings of the First Workshop on Computational Approaches to Compound Analysis (ComAComA 2014)

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Distinguishing Degrees of Compositionality in Compound Splitting for Statistical Machine Translation
Marion Weller | Fabienne Cap | Stefan Müller | Sabine Schulte im Walde | Alexander Fraser
Proceedings of the First Workshop on Computational Approaches to Compound Analysis (ComAComA 2014)

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A Database of Paradigmatic Semantic Relation Pairs for German Nouns, Verbs, and Adjectives
Silke Scheible | Sabine Schulte im Walde
Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing

2013

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Exploring Vector Space Models to Predict the Compositionality of German Noun-Noun Compounds
Sabine Schulte im Walde | Stefan Müller | Stefan Roller
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

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A Multimodal LDA Model integrating Textual, Cognitive and Visual Modalities
Stephen Roller | Sabine Schulte im Walde
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Using subcategorization knowledge to improve case prediction for translation to German
Marion Weller | Alexander Fraser | Sabine Schulte im Walde
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Regular Meaning Shifts in German Particle Verbs: A Case Study
Sylvia Springorum | Jason Utt | Sabine Schulte im Walde
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

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The (Un)expected Effects of Applying Standard Cleansing Models to Human Ratings on Compositionality
Stephen Roller | Sabine Schulte im Walde | Silke Scheible
Proceedings of the 9th Workshop on Multiword Expressions

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Potential and limits of distributional approaches for semantic relatedness
Sabine Schulte in Walde
Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora

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Uncovering Distributional Differences between Synonyms and Antonyms in a Word Space Model
Silke Scheible | Sabine Schulte im Walde | Sylvia Springorum
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Detecting Polysemy in Hard and Soft Cluster Analyses of German Preposition Vector Spaces
Sylvia Springorum | Sabine Schulte im Walde | Jason Utt
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Automatic classification of German an particle verbs
Sylvia Springorum | Sabine Schulte im Walde | Antje Roßdeutscher
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

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Association Norms of German Noun Compounds
Sabine Schulte im Walde | Susanne Borgwaldt | Ronny Jauch
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

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Modeling Regular Polysemy: A Study on the Semantic Classification of Catalan Adjectives
Gemma Boleda | Sabine Schulte im Walde | Toni Badia
Computational Linguistics, Volume 38, Issue 3 - September 2012

2010

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BabyExp: Constructing a Huge Multimodal Resource to Acquire Commonsense Knowledge Like Children Do
Massimo Poesio | Marco Baroni | Oswald Lanz | Alessandro Lenci | Alexandros Potamianos | Hinrich Schütze | Sabine Schulte im Walde | Luca Surian
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

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Comparing Computational Models of Selectional Preferences - Second-order Co-Occurrence vs. Latent Semantic Clusters
Sabine Schulte im Walde
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

2009

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Proceedings of the ACL-IJCNLP 2009 Software Demonstrations
Gary Geunbae Lee | Sabine Schulte im Walde
Proceedings of the ACL-IJCNLP 2009 Software Demonstrations

2008

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Evaluating a German Sketch Grammar: A Case Study on Noun Phrase Case
Kremena Ivanova | Ulrich Heid | Sabine Schulte im Walde | Adam Kilgarriff | Jan Pomikálek
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

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Corpus Co-Occurrence, Dictionary and Wikipedia Entries as Resources for Semantic Relatedness Information
Michael Roth | Sabine Schulte im Walde
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

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Coling 2008: Proceedings of the workshop on Human Judgements in Computational Linguistics
Ron Artstein | Gemma Boleda | Frank Keller | Sabine Schulte im Walde
Coling 2008: Proceedings of the workshop on Human Judgements in Computational Linguistics

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Combining EM Training and the MDL Principle for an Automatic Verb Classification Incorporating Selectional Preferences
Sabine Schulte im Walde | Christian Hying | Christian Scheible | Helmut Schmid
Proceedings of ACL-08: HLT

2007

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Modelling Polysemy in Adjective Classes by Multi-Label Classification
Gemma Boleda | Sabine Schulte im Walde | Toni Badia
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Experiments on the Automatic Induction of German Semantic Verb Classes
Sabine Schulte im Walde
Computational Linguistics, Volume 32, Number 2, June 2006

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Characterizing Response Types and Revealing Noun Ambiguity in German Association Norms
Alissa Melinger | Sabine Schulte im Walde | Andrea Weber
Proceedings of the Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together

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Can Human Verb Associations Help Identify Salient Features for Semantic Verb Classification?
Sabine Schulte im Walde
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

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Human Verb Associations as the Basis for Gold Standard Verb Classes: Validation against GermaNet and FrameNet
Sabine Schulte im Walde
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

2005

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Morphology vs. Syntax in Adjective Class Acquisition
Gemma Boleda | Toni Badia | Sabine Schulte im Walde
Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition

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Identifying Semantic Relations and Functional Properties of Human Verb Associations
Sabine Schulte im Walde | Alissa Melinger
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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Identification, Quantitative Description, and Preliminary Distributional Analysis of German Particle Verbs
Sabine Schulte im Walde
Proceedings of the Workshop on Enhancing and Using Electronic Dictionaries

2003

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Experiments on the Choice of Features for Learning Verb Classes
Sabine Schulte im Walde
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Spectral Clustering for German Verbs
Chris Brew | Sabine Schulte im Walde
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

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A Subcategorisation Lexicon for German Verbs induced from a Lexicalised PCFG
Sabine Schulte im Walde
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Acquiring Lexical Knowledge for Anaphora Resolution
Massimo Poesio | Tomonori Ishikawa | Sabine Schulte im Walde | Renata Vieira
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Inducing German Semantic Verb Classes from Purely Syntactic Subcategorisation Information
Sabine Schulte im Walde | Chris Brew
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

2000

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Robust German Noun Chunking With a Probabilistic Context-Free Grammar
Helmut Schmid | Sabine Schulte im Walde
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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Clustering Verbs Semantically According to their Alternation Behaviour
Sabine Schulte im Walde
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics