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Metodologija rešavanja semantičkih problema u obradi kratkih tekstova napisanih na prirodnim jezicima sa ograničenim resursima
Statistički pristupi obradi prirodnih jezika tipično zahtevaju značajne količine anotiranih podataka, a često i različite pomoćne jezičke alate, što ograničava njihovu primenu u resursno ograničenim situacijama. U ovoj disertaciji predstavljena je metodologija razvoja statističkih rešenja u semantičkoj obradi prirodnih jezika sa ograničenim resursima. Ovakvi jezici se odlikuju ne samo limitiranim brojem postojećih jezičkih resursa, već i ograničenim mogućnostima za razvoj novih skupova podataka i namenskih alata i algoritama.
Predložena metodologija je usredsređena na kratke tekstove zbog njihove rasprostranjenosti u digitalnoj komunikaciji i zbog veće složenosti njihove semantičke obrade. Metodologija obuhvata sve faze izrade statističkih rešenja, od prikupljanja tekstualnog sadržaja, preko anotacije podataka, do formulisanja, obučavanja i evaluacije modela mašinskog učenja. Njena upotreba je detaljno ilustrovana na dva semantička problema – analizi sentimenta i određivanju semantičke sličnosti. Kao primer jezika sa ograničenim resursima korišćen je srpski jezik, ali se predložena metodologija može primeniti i na druge jezike iz ove kategorije.
Pored opšte metodologije, u doprinose ove disertacije spada razvoj novog, fleksibilnog sistema označavanja sentimenta kratkih tekstova, nove metrike za utvrđivanje ekonomičnosti anotacije, kao i nekoliko novih modela za određivanje semantičke sličnosti kratkih tekstova. Rezultati disertacije uključuju i kreiranje prvih javno dostupnih anotiranih skupova podataka za probleme analize sentimenta i određivanja semantičke sličnosti kratkih tekstova na srpskom jeziku, razvoj i evaluaciju većeg broja modela na ovim problemima, i prvu komparativnu evaluaciju većeg broja alata za morfološku normalizaciju na kratkim tekstovima na srpskom jeziku.
Predložena metodologija je usredsređena na kratke tekstove zbog njihove rasprostranjenosti u digitalnoj komunikaciji i zbog veće složenosti njihove semantičke obrade. Metodologija obuhvata sve faze izrade statističkih rešenja, od prikupljanja tekstualnog sadržaja, preko anotacije podataka, do formulisanja, obučavanja i evaluacije modela mašinskog učenja. Njena upotreba je detaljno ilustrovana na dva semantička problema – analizi sentimenta i određivanju semantičke sličnosti. Kao primer jezika sa ograničenim resursima korišćen je srpski jezik, ali se predložena metodologija može primeniti i na druge jezike iz ove kategorije.
Pored opšte metodologije, u doprinose ove disertacije spada razvoj novog, fleksibilnog sistema označavanja sentimenta kratkih tekstova, nove metrike za utvrđivanje ekonomičnosti anotacije, kao i nekoliko novih modela za određivanje semantičke sličnosti kratkih tekstova. Rezultati disertacije uključuju i kreiranje prvih javno dostupnih anotiranih skupova podataka za probleme analize sentimenta i određivanja semantičke sličnosti kratkih tekstova na srpskom jeziku, razvoj i evaluaciju većeg broja modela na ovim problemima, i prvu komparativnu evaluaciju većeg broja alata za morfološku normalizaciju na kratkim tekstovima na srpskom jeziku.
Doktorska disertacija, Univerzitet u Beogradu - Elektrotehnički fakultet, 2020.
A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts
Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.
In PLoS ONE, 2020.
SETimes.SR – A Reference Training Corpus of Serbian
In this paper we present SETimes.SR – a gold standard dataset for Serbian, annotated with regard to document, sentence, and token segmentation, morphosyntax, lemmas, dependency syntax, and named entities. We describe the annotation layers and provide a basic statistical overview of them, and we discuss the method of encoding them in the CoNLL and the TEI format. In addition, we compare the SETimes.SR corpus with the older SETimes.HR dataset in Croatian.
JT-DH, 2018.
Fine-grained Semantic Textual Similarity for Serbian
Although the task of semantic textual similarity (STS) has gained in prominence in the last few years, annotated STS datasets for model training and evaluation, particularly those with fine-grained similarity scores, remain scarce for languages other than English, and practically non-existent for minor ones. In this paper, we present the Serbian Semantic Textual Similarity News Corpus (STS.news.sr) – an STS dataset for Serbian that contains 1192 sentence pairs annotated with fine-grained semantic similarity scores. We describe the process of its creation and annotation, and we analyze and compare our corpus with the existing news-based STS datasets in English and other major languages. Several existing STS models are evaluated on the Serbian STS News Corpus, and a new supervised bag-of-words model that combines part-of-speech weighting with term frequency weighting is proposed and shown to outperform similar methods. Since Serbian is a morphologically rich language, the effect of various morphological normalization tools on STS model performances is considered as well. The Serbian STS News Corpus, the annotation tool and guidelines used in its creation, and the STS model framework used in the evaluation are all made publicly available.
LREC, 2018.
Više informacija
PDF
Programski kod
Skup podataka
Alat za anotaciju STSAnno
Uputstva za anotaciju semantičke sličnosti kratkih tekstova
Srpski web korpus srWaC
ReLDI tokenizator za srpski
Stemeri za srpski i hrvatski
BTagger za srpski
HunPos i CST modeli za hrvatski
ReLDI tager i lematizator za srpski i hrvatski
Sentiment Classification of Documents in Serbian: The Effects of Morphological Normalization
Sentiment classification of texts written in Serbian is still an under-researched topic. One of the open issues is how the different forms of morphological normalization affect the performances of different sentiment classifiers and which normalization procedure is optimal for this task. In this paper we assess and compare the impact of lemmatizers and stemmers for Serbian on classifiers trained and evaluated on the Serbian Movie Review Dataset.
TELFOR, 2016.
Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The Serbian Movie Review Dataset
Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant systematic differences between the sentiment classes. We prove its superiority over the random sampling method and we use it to create the Serbian movie review dataset – SerbMR – the first balanced and topically uniform sentiment analysis dataset in Serbian. In addition, we propose an incremental way of finding the optimal combination of simple text processing options and machine learning features for sentiment classification. Several popular classifiers are used in conjunction with this evaluation approach in order to establish strong but reliable baselines for sentiment analysis in Serbian.
LREC, 2016.
Using Part-of-Speech Tags as Deep-Syntax Indicators in Determining Short-Text Semantic Similarity
This paper presents POST STSS, a method of determining short-text semantic similarity in which part-of-speech tags are used as indicators of the deeper syntactic information usually extracted by more advanced tools like parsers and semantic role labelers. Our model employs a part-of-speech weighting scheme and is based on a statistical bag-of-words approach. It does not require either hand-crafted knowledge bases or advanced syntactic tools, which makes it easily applicable to languages with limited natural language processing resources. By using a paraphrase recognition test, we demonstrate that our system achieves a higher accuracy than all existing statistical similarity algorithms and solutions of a more structural kind.
ComSIS, 2015.
Semantic similarity of short texts in languages with a deficient natural language processing support
Measuring the semantic similarity of short texts is a noteworthy problem since short texts are widely used on the Internet, in the form of product descriptions or captions, image and webpage tags, news headlines, etc. This paper describes a methodology which can be used to create a software system capable of determining the semantic similarity of two given short texts. The proposed LInSTSS approach is particularly suitable for application in situations when no large, publicly available, electronic linguistic resources can be found for the desired language. We describe the basic working principles of the system architecture we propose, as well as the stages of its construction and use. Also, we explain the procedure used to generate a paraphrase corpus which is then utilized in the evaluation process. Finally, we analyze the evaluation results obtained from a system created for the Serbian language, and we discuss possible improvements which would increase system accuracy.
Decision Support Systems, 2013.
