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Found 28 papers, 2 papers with code
Exploring Neural Text Simplification Models
Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction.
Ranked #14 on
Text Simplification
on TurkCorpus
Sentence Alignment Methods for Improving Text Simplification Systems
We provide several methods for sentence-alignment of texts with different complexity levels.
Word Embeddings-Based Uncertainty Detection in Financial Disclosures
In this paper, we use NLP techniques to detect linguistic uncertainty in financial disclosures.
Effects of Lexical Properties on Viewing Time per Word in Autistic and Neurotypical Readers
Eye tracking studies from the past few decades have shaped the way we think of word complexity and cognitive load: words that are long, rare and ambiguous are more difficult to read.
Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs
Complexity of texts is usually assessed only at the lexical and syntactic levels.
CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups
Complex word identification (CWI) is an important task in text accessibility.
Multilingual and Cross-Lingual Complex Word Identification
Complex Word Identification (CWI) is an important task in lexical simplification and text accessibility.
Diachronic Changes in Text Complexity in 20th Century English Language: An NLP Approach
In British English, we compared the complexity of texts published in 1931, 1961 and 1991, while in American English we compared the complexity of texts published in 1961 and 1992.
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations
In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot.
Ranked #2 on
Emotion Recognition in Conversation
on EC
Use of Domain-Specific Language Resources in Machine Translation
In this paper, we address the problem of Machine Translation (MT) for a specialised domain in a language pair for which only a very small domain-specific parallel corpus is available.
Bootstrapping a Hybrid MT System to a New Language Pair
The usual concern when opting for a rule-based or a hybrid machine translation (MT) system is how much effort is required to adapt the system to a different language pair or a new domain.
A Spreading Activation Framework for Tracking Conceptual Complexity of Texts
We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation.
Automated Text Simplification as a Preprocessing Step for Machine Translation into an Under-resourced Language
We use the state-of-the-art automatic text simplification (ATS) system for lexically and syntactically simplifying source sentences, which are then translated with two state-of-the-art English-to-Serbian MT systems, the phrase-based MT (PBMT) and the neural MT (NMT).
