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Found 96 papers, 15 papers with code
Regression Analysis of Lexical and Morpho-Syntactic Properties of Kiezdeutsch
Kiezdeutsch is a variety of German predominantly spoken by teenagers from multi-ethnic urban neighborhoods in casual conversations with their peers.
Figurative Language in Noun Compound Models across Target Properties, Domains and Time
A variety of distributional and multi-modal computational approaches has been suggested for modelling the degrees of compositionality across types of multiword expressions and languages.
Investigating Independence vs. Control: Agenda-Setting in Russian News Coverage on Social Media
Agenda-setting is a widely explored phenomenon in political science: powerful stakeholders (governments or their financial supporters) have control over the media and set their agenda: political and economical powers determine which news should be salient.
DiaWUG: A Dataset for Diatopic Lexical Semantic Variation in Spanish
We provide a novel dataset – DiaWUG – with judgements on diatopic lexical semantic variation for six Spanish variants in Europe and Latin America.
Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the Evaluative Meaning of German Personal Name Compounds
We present a comprehensive computational study of the under-investigated phenomenon of personal name compounds (PNCs) in German such as Willkommens-Merkel ('Welcome-Merkel').
A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement
We present a novel dataset for physical and abstract plausibility of events in English.
Semantics of Multiword Expressions in Transformer-Based Models: A Survey
Our findings overall question the ability of transformer models to robustly capture fine-grained semantics.
The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change
We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface.
Investigating the Nature of Disagreements on Mid-Scale Ratings: A Case Study on the Abstractness-Concreteness Continuum
Humans tend to strongly agree on ratings on a scale for extreme cases (e. g., a CAT is judged as very concrete), but judgements on mid-scale words exhibit more disagreement.
Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain
We propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task style setting to overcome the lack of annotated datasets.
What Drives the Use of Metaphorical Language? Negative Insights from Abstractness, Affect, Discourse Coherence and Contextualized Word Representations
Given a specific discourse, which discourse properties trigger the use of metaphorical language, rather than using literal alternatives?
Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality
First, is a metaphorically-perceived discourse more abstract and more emotional in comparison to a literally-perceived discourse?
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains
Predicting the difficulty of domain-specific vocabulary is an important task towards a better understanding of a domain, and to enhance the communication between lay people and experts.
Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model
We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology, physics and social sciences.
Lexical Semantic Change Discovery
While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models.
More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods
This paper presents a comparison of unsupervised methods of hypernymy prediction (i. e., to predict which word in a pair of words such as fish-cod is the hypernym and which the hyponym).
Explaining and Improving BERT Performance on Lexical Semantic Change Detection
Type- and token-based embedding architectures are still competing in lexical semantic change detection.
CL-IMS @ DIACR-Ita: Volente o Nolente: BERT does not outperform SGNS on Semantic Change Detection
We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian.
OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection
We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian.
IMS at SemEval-2020 Task 1: How low can you go? Dimensionality in Lexical Semantic Change Detection
We present the results of our system for SemEval-2020 Task 1 that exploits a commonly used lexical semantic change detection model based on Skip-Gram with Negative Sampling.
Predicting Degrees of Technicality in Automatic Terminology Extraction
While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied.
CCOHA: Clean Corpus of Historical American English
Modelling language change is an increasingly important area of interest within the fields of sociolinguistics and historical linguistics.
Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds
Predicting the degree of compositionality of noun compounds such as {``}snowball{''} and {``}butterfly{''} is a crucial ingredient for lexicography and Natural Language Processing applications, to know whether the compound should be treated as a whole, or through its constituents, and what it means.
A Domain-Specific Dataset of Difficulty Ratings for German Noun Compounds in the Domains DIY, Cooking and Automotive
We present a dataset with difficulty ratings for 1, 030 German closed noun compounds extracted from domain-specific texts for do-it-ourself (DIY), cooking and automotive.
Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction
We perform a comparative study for automatic term extraction from domain-specific language using a PageRank model with different edge-weighting methods.
Simulating Lexical Semantic Change from Sense-Annotated Data
We present a novel procedure to simulate lexical semantic change from synchronic sense-annotated data, and demonstrate its usefulness for assessing lexical semantic change detection models.
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP
Information about individuals can help to better understand what they say, particularly in social media where texts are short.
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains
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.
Second-order Co-occurrence Sensitivity of Skip-Gram with Negative Sampling
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.
SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction
We introduce SURel, a novel dataset with human-annotated meaning shifts between general-language and domain-specific contexts.
Distributional Interaction of Concreteness and Abstractness in Verb--Noun Subcategorisation
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.
Fine-Grained Termhood Prediction for German Compound Terms Using Neural Networks
Automatic term identification and investigating the understandability of terms in a specialized domain are often treated as two separate lines of research.
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains
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.
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
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.
A Laypeople Study on Terminology Identification across Domains and Task Definitions
This paper introduces a new dataset of term annotation.
Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs
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.
Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models
We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs.
Assessing Meaning Components in German Complex Verbs: A Collection of Source-Target Domains and Directionality
This paper presents a collection to assess meaning components in German complex verbs, which frequently undergo meaning shifts.
Quantitative Semantic Variation in the Contexts of Concrete and Abstract Words
Across disciplines, researchers are eager to gain insight into empirical features of abstract vs. concrete concepts.
Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution
Cases of coreference and bridging resolution often require knowledge about semantic relations between anaphors and antecedents.
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models.
Cross-Lingual Sentiment Classification
Machine Translation
+5
Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change
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.
Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness
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.
Distribution-based Prediction of the Degree of Grammaticalization for German Prepositions
We test the hypothesis that the degree of grammaticalization of German prepositions correlates with their corpus-based contextual dispersion measured by word entropy.
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
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).
Hierarchical Embeddings for Hypernymy Detection and Directionality
We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality.
German in Flux: Detecting Metaphoric Change via Word Entropy
This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change.
Complex Verbs are Different: Exploring the Visual Modality in Multi-Modal Models to Predict Compositionality
This paper compares a neural network DSM relying on textual co-occurrences with a multi-modal model integrating visual information.
Factoring Ambiguity out of the Prediction of Compositionality for German Multi-Word Expressions
Ambiguity represents an obstacle for distributional semantic models(DSMs), which typically subsume the contexts of all word senses within one vector.
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses
Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words.
Addressing Problems across Linguistic Levels in SMT: Combining Approaches to Model Morphology, Syntax and Lexical Choice
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.
Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language
Up to date, the majority of computational models still determines the semantic relatedness between words (or larger linguistic units) on the type level.
Evaluating the Reliability and Interaction of Recursively Used Feature Classes for Terminology Extraction
Feature design and selection is a crucial aspect when treating terminology extraction as a machine learning classification problem.
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems.
GhoSt-PV: A Representative Gold Standard of German Particle Verbs
German particle verbs represent a frequent type of multi-word-expression that forms a highly productive paradigm in the lexicon.
Neural-based Noise Filtering from Word Embeddings
Word embeddings have been demonstrated to benefit NLP tasks impressively.
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction
We propose a novel vector representation that integrates lexical contrast into distributional vectors and strengthens the most salient features for determining degrees of word similarity.
Automatically Generated Affective Norms of Abstractness, Arousal, Imageability and Valence for 350 000 German Lemmas
This paper presents a collection of 350, 000 German lemmatised words, rated on four psycholinguistic affective attributes.
Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification
Vector space models and distributional information are widely used in NLP.
GhoSt-NN: A Representative Gold Standard of German Noun-Noun Compounds
This paper presents a novel gold standard of German noun-noun compounds (Ghost-NN) including 868 compounds annotated with corpus frequencies of the compounds and their constituents, productivity and ambiguity of the constituents, semantic relations between the constituents, and compositionality ratings of compound-constituent pairs.
Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality
In the work presented here we assess the degree of compositionality of German Particle Verbs with a Distributional Semantics Model which only relies on word window information and has no access to syntactic information as such.
Fuzzy V-Measure - An Evaluation Method for Cluster Analyses of Ambiguous Data
This paper discusses an extension of the V-measure (Rosenberg and Hirschberg, 2007), an entropy-based cluster evaluation metric.
A Rank-based Distance Measure to Detect Polysemy and to Determine Salient Vector-Space Features for German Prepositions
This paper addresses vector space models of prepositions, a notoriously ambiguous word class.
Automatic Extraction of Synonyms for German Particle Verbs from Parallel Data with Distributional Similarity as a Re-Ranking Feature
In our evaluation against a gold standard, we compare different pre-processing strategies (lemmatized vs. inflected forms) and introduce language model scores of synonym candidates in the context of the input particle verb as well as distributional similarity as additional re-ranking criteria.
Automatic classification of German \textitan particle verbs
A focus of the study was on the mutual profit of theoretical and empirical perspectives with respect to salient semantic properties of the an particle verbs: (a) how can we transform the theoretical insights into empirical, corpus-based features, (b) to what extent can we replicate the theoretical classification by a machine learning approach, and (c) can the computational analysis in turn deepen our insights to the semantic properties of the particle verbs?
Association Norms of German Noun Compounds
This paper introduces association norms of German noun compounds as a lexical semantic resource for cognitive and computational linguistics research on compositionality.
