Search Results for author:
Found 14 papers, 1 papers with code
Identifying Draft Bills Impacting Existing Legislation: a Case Study on Romanian
In our paper, we present a novel corpus of historical legal documents on the Romanian public procurement legislation and an annotated subset of draft bills that have been screened by legal experts and identified as impacting past public procurement legislation.
CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts
Traditional text complexity assessment usually takes into account only syntactic and lexical text complexity.
Content Extraction and Lexical Analysis from Customer-Agent Interactions
In this paper, we provide a lexical comparative analysis of the vocabulary used by customers and agents in an Enterprise Resource Planning (ERP) environment and a potential solution to clean the data and extract relevant content for NLP.
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
A Visual Representation of Wittgenstein's Tractatus Logico-Philosophicus
In this paper we present a data visualization method together with its potential usefulness in digital humanities and philosophy of language.
Vanilla Classifiers for Distinguishing between Similar Languages
In this paper we describe the submission of the UniBuc-NLP team for the Discriminating between Similar Languages Shared Task, DSL 2016.
On the Similarities Between Native, Non-native and Translated Texts
We present a computational analysis of three language varieties: native, advanced non-native, and translation.
Comparing Speech and Text Classification on ICNALE
In this paper we explore and compare a speech and text classification approach on a corpus of native and non-native English speakers.
Using Word Embeddings to Translate Named Entities
In this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant to the task at hand, introducing a novel, yet simple way of obtaining bilingual word vectors.
Chinese Named Entity Recognition
named-entity-recognition
+4
A Corpus of Native, Non-native and Translated Texts
We describe a monolingual English corpus of original and (human) translated texts, with an accurate annotation of speaker properties, including the original language of the utterances and the speaker{'}s country of origin.
