Sergiu Nisioi


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Identifying Draft Bills Impacting Existing Legislation: a Case Study on Romanian
Corina Ceausu | Sergiu Nisioi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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. Using the manual annotations provided by the experts, we attempt to automatically identify future draft bills that have the potential to impact existing policies on public procurement.


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CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts
Sanja Stajner | Sergiu Nisioi | Ioana Hulpuș
Proceedings of the Twelfth Language Resources and Evaluation Conference

Traditional text complexity assessment usually takes into account only syntactic and lexical text complexity. The task of automatic assessment of conceptual text complexity, important for maintaining reader’s interest and text adaptation for struggling readers, has only been proposed recently. In this paper, we present CoCo - a tool for automatic assessment of conceptual text complexity, based on using the current state-of-the-art unsupervised approach. We make the code and API freely available for research purposes, and describe the code and the possibility for its personalization and adaptation in details. We compare the current implementation with the state of the art, discussing the influence of the choice of entity linker on the performances of the tool. Finally, we present results obtained on two widely used text simplification corpora, discussing the full potential of the tool.


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Content Extraction and Lexical Analysis from Customer-Agent Interactions
Sergiu Nisioi | Anca Bucur | Liviu P. Dinu
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text

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. As a result, we demonstrate that the actual vocabulary for the language that prevails in the ERP conversations is highly divergent from the standardized dictionary and further different from general language usage as extracted from the Common Crawl corpus. Moreover, in specific business communication circumstances, where it is expected to observe a high usage of standardized language, code switching and non-standard expression are predominant, emphasizing once more the discrepancy between the day-to-day use of language and the standardized one.

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A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification
Sanja Štajner | Sergiu Nisioi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Exploring Neural Text Simplification Models
Sergiu Nisioi | Sanja Štajner | Simone Paolo Ponzetto | Liviu P. Dinu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems


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On the Similarities Between Native, Non-native and Translated Texts
Ella Rabinovich | Sergiu Nisioi | Noam Ordan | Shuly Wintner
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Using Word Embeddings to Translate Named Entities
Octavia-Maria Şulea | Sergiu Nisioi | Liviu P. Dinu
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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. Inspired by observations in (Mikolov et al., 2013b), which show that training their word vector model on comparable corpora yields comparable vector space representations of those corpora, reducing the problem of translating words to finding a rotation matrix, and results in (Zou et al., 2013), which showed that bilingual word embeddings can improve Chinese Named Entity Recognition (NER) and English to Chinese phrase translation, we use the sentence-aligned English-French EuroParl corpora and show that word embeddings extracted from a merged corpus (corpus resulted from the merger of the two aligned corpora) can be used to NE translation. We extrapolate that word embeddings trained on merged parallel corpora are useful in Named Entity Recognition and Translation tasks for resource-poor languages.

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Comparing Speech and Text Classification on ICNALE
Sergiu Nisioi
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we explore and compare a speech and text classification approach on a corpus of native and non-native English speakers. We experiment on a subset of the International Corpus Network of Asian Learners of English containing the recorded speeches and the equivalent text transcriptions. Our results suggest a high correlation between the spoken and written classification results, showing that native accent is highly correlated with grammatical structures found in text.

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A Corpus of Native, Non-native and Translated Texts
Sergiu Nisioi | Ella Rabinovich | Liviu P. Dinu | Shuly Wintner
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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. We thus obtain three sub-corpora of texts reflecting native English, non-native English, and English translated from a variety of European languages. This dataset will facilitate the investigation of similarities and differences between these kinds of sub-languages. Moreover, it will facilitate a unified comparative study of translations and language produced by (highly fluent) non-native speakers, two closely-related phenomena that have only been studied in isolation so far.

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A Visual Representation of Wittgenstein’s Tractatus Logico-Philosophicus
Anca Bucur | Sergiu Nisioi
Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)

In this paper we will discuss a method for data visualization together with its potential usefulness in digital humanities and philosophy of language. We compiled a multilingual parallel corpus from different versions of Wittgenstein’s Tractatus Logico-philosophicus, including the original in German and translations into English, Spanish, French, and Russian. Using this corpus, we compute a similarity measure between propositions and render a visual network of relations for different languages.

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Vanilla Classifiers for Distinguishing between Similar Languages
Sergiu Nisioi | Alina Maria Ciobanu | Liviu P. Dinu
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

In this paper we describe the submission of the UniBuc-NLP team for the Discriminating between Similar Languages Shared Task, DSL 2016. We present and analyze the results we obtained in the closed track of sub-task 1 (Similar languages and language varieties) and sub-task 2 (Arabic dialects). For sub-task 1 we used a logistic regression classifier with tf-idf feature weighting and for sub-task 2 a character-based string kernel with an SVM classifier. Our results show that good accuracy scores can be obtained with limited feature and model engineering. While certain limitations are to be acknowledged, our approach worked surprisingly well for out-of-domain, social media data, with 0.898 accuracy (3rd place) for dataset B1 and 0.838 accuracy (4th place) for dataset B2.


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On the syllabic structures of Aromanian
Sergiu Nisioi
Proceedings of the 8th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH)


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A clustering approach for translationese identification
Sergiu Nisioi | Liviu P. Dinu
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013


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Authorial Studies using Ranked Lexical Features
Liviu P. Dinu | Sergiu Nisioi
Proceedings of COLING 2012: Demonstration Papers