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Found 45 papers, 12 papers with code
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings
The evaluation results of our system on the test set is 88. 1{\%} (79. 0{\%} for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76. 0{\%} (42. 3{\%} when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations.
A Neural Architecture for Dialectal Arabic Segmentation
The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general.
Sketching Word Vectors Through Hashing
We propose a new fast word embedding technique using hash functions.
Projection Al\'eatoire Non-N\'egative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding
Non-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique.
Learning from Relatives: Unified Dialectal Arabic Segmentation
Arabic dialects do not just share a common koin{\'e}, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other.
HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment
This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity.
Coarse Lexical Frame Acquisition at the Syntax--Semantics Interface Using a Latent-Variable PCFG Model
We present a method for unsupervised lexical frame acquisition at the syntax{--}semantics interface.
German and French Neural Supertagging Experiments for LTAG Parsing
We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars.
TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions
We describe the TRAPACC system and its variant TRAPACCS that participated in the closed track of the PARSEME Shared Task 2018 on labeling verbal multiword expressions (VMWEs).
Object-oriented lexical encoding of multiword expressions: Short and sweet
Multiword expressions (MWEs) exhibit both regular and idiosyncratic properties.
Towards a Compositional Analysis of German Light Verb Constructions (LVCs) Combining Lexicalized Tree Adjoining Grammar (LTAG) with Frame Semantics
Complex predicates formed of a semantically {`}light{'} verbal head and a noun or verb which contributes the major part of the meaning are frequently referred to as {`}light verb constructions{'} (LVCs).
SemEval-2019 Task 2: Unsupervised Lexical Frame Induction
This paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019.
A Neural Graph-based Approach to Verbal MWE Identification
We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach.
A multi-lingual and cross-domain analysis of features for text simplification
In text simplification and readability research, several features have been proposed to estimate or simplify a complex text, e. g., readability scores, sentence length, or proportion of POS tags.
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors
In this system demonstration paper, we present an open-source web-based application with a responsive design for modular semantic frame annotation (SFA).
Supervised Disambiguation of German Verbal Idioms with a BiLSTM Architecture
Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation.
Statistical Parsing of Tree Wrapping Grammars
We describe an approach to statistical parsing with Tree-Wrapping Grammars (TWG).
Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation
We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation, relying on count-based and vector-based features as well as perplexity-based language model features, which are intended to reflect each alternation{'}s felicity by simulating it.
Probing for Constituency Structure in Neural Language Models
We find that 4 pretrained transfomer LMs obtain high performance on our probing tasks even on manipulated data, suggesting that semantic and syntactic knowledge in their representations can be separated and that constituency information is in fact learned by the LM.
DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification
13k sentence pairs) and a web-domain corpus (approx.
Ranked #1 on
Text Simplification
on DEplain-APA-doc
Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building
In this paper, we describe our submission to the BabyLM Challenge 2023 shared task on data-efficient language model (LM) pretraining (Warstadt et al., 2023).
Multilingual Nonce Dependency Treebanks: Understanding how LLMs represent and process syntactic structure
We replicate the findings of M\"uller-Eberstein et al. (2022) on nonce test data and show that the performance declines on both MLMs and ALMs wrt.
Dissecting Paraphrases: The Impact of Prompt Syntax and supplementary Information on Knowledge Retrieval from Pretrained Language Models
Typically, designing these prompts is a tedious task because small differences in syntax or semantics can have a substantial impact on knowledge retrieval performance.
An Analysis of Attention in German Verbal Idiom Disambiguation
In this paper we examine a BiLSTM architecture for disambiguating verbal potentially idiomatic expressions (PIEs) as to whether they are used in a literal or an idiomatic reading with respect to explainability of its decisions.
TS-ANNO: An Annotation Tool to Build, Annotate and Evaluate Text Simplification Corpora
TS-ANNO can be used for i) sentence–wise alignment, ii) rating alignment pairs (e. g., w. r. t.
Implicit representations of event properties within contextual language models: Searching for “causativity neurons”
This paper addresses the question to which extent neural contextual language models such as BERT implicitly represent complex semantic properties.
RRGparbank: A Parallel Role and Reference Grammar Treebank
This paper describes the first release of RRGparbank, a multilingual parallel treebank for Role and Reference Grammar (RRG) containing annotations of George Orwell’s novel 1984 and its translations.
Improving Low-resource RRG Parsing with Cross-lingual Self-training
Starting from an existing RRG parser, we propose two strategies for low-resource parsing: first, we extend the parsing model into a cross-lingual parser, exploiting the parallel data in the high-resource language and unsupervised word alignments by providing internal states of the source-language parser to the target-language parser.
