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Found 18 papers, 0 papers with code
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.
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.
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms.
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.
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.
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.
Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification
Vector space models and distributional information are widely used in NLP.
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.
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.
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.
