Selected Publications
A methodology for solving semantic tasks in the processing of short texts written in natural languages with limited resources
Statistical approaches to natural language processing typically require considerable amounts of labeled data, and often various auxiliary language tools as well, limiting their applicability in resource-limited settings. This thesis presents a methodology for developing statistical solutions in the semantic processing of natural languages with limited resources. In these languages, not only are existing language resources limited, but so are the capabilities for developing new datasets and dedicated tools and algorithms. The proposed methodology focuses on short texts due to their prevalence in digital communication, as well as the greater complexity regarding their semantic processing.
The methodology encompasses all phases in the creation of statistical solutions, from the collection of textual content, to data annotation, to the formulation, training, and evaluation of machine learning models. Its use is illustrated in detail on two semantic tasks – sentiment analysis and semantic textual similarity. The Serbian language is utilized as an example of a language with limited resources, but the proposed methodology can also be applied to other languages in this category.
In addition to the general methodology, the contributions of this thesis consist of the development of a new, flexible short-text sentiment annotation system, a new annotation cost-effectiveness metric, as well as several new semantic textual similarity models. The thesis results also include the creation of the first publicly available annotated datasets of short texts in Serbian for the tasks of sentiment analysis and semantic textual similarity, the development and evaluation of numerous models on these tasks, and the first comparative evaluation of multiple morphological normalization tools on short texts in Serbian.
The methodology encompasses all phases in the creation of statistical solutions, from the collection of textual content, to data annotation, to the formulation, training, and evaluation of machine learning models. Its use is illustrated in detail on two semantic tasks – sentiment analysis and semantic textual similarity. The Serbian language is utilized as an example of a language with limited resources, but the proposed methodology can also be applied to other languages in this category.
In addition to the general methodology, the contributions of this thesis consist of the development of a new, flexible short-text sentiment annotation system, a new annotation cost-effectiveness metric, as well as several new semantic textual similarity models. The thesis results also include the creation of the first publicly available annotated datasets of short texts in Serbian for the tasks of sentiment analysis and semantic textual similarity, the development and evaluation of numerous models on these tasks, and the first comparative evaluation of multiple morphological normalization tools on short texts in Serbian.
PhD Thesis, University of Belgrade - School of Electrical Engineering, 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.
In 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.
In LREC, 2018.
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.
In 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.
In 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.
In 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.
In Decision Support Systems, 2013.
