Abstractive Summarization as Augmentation for Document-Level Event Detection

Transformer-based models have consistently produced substantial performance gains across a variety of NLP tasks, compared to shallow models. However, deep models are orders of magnitude more computationally expensive than shallow models, especially on tasks with large sequence lengths, such as document-level event detection. In this work, we attempt to bridge the performance gap between shallow and deep models on document-level event detection by using abstractive text summarization as an augmentation method. We augment the DocEE dataset by generating abstractive summaries of examples from low-resource classes. For classification, we use linear SVM with TF-IDF representations and RoBERTa-base. We use BART for zero-shot abstractive summarization, making our augmentation setup less resource-intensive compared to supervised fine-tuning. We experiment with four decoding methods for text generation, namely beam search, top-k sampling, top-p sampling, and contrastive search. Furthermore, we investigate the impact of using document titles as additional input for classification. Our results show that using the document title offers 2.04% and 3.19% absolute improvement in macro F1-score for linear SVM and RoBERTa, respectively. Augmentation via summarization further improves the performance of linear SVM by about 0.5%, varying slightly across decoding methods. Overall, our augmentation setup yields insufficient improvements for linear SVM compared to RoBERTa

Object Tracking Using Camera Mounted on Mobile Robot

.

Extraction of Articles from News Portals Using Machine Learning

Revolucijom novih tehnologija, primarno pametnih mobilnih uređaja, omogućena je konstantna i neprekidna komunikacija. Neprekidna komunikacija različitom vrstom medija, poput teksta i slika, stvara neprekidan tok podataka. Ti se podaci trebaju obraditi. Ekstrakcija informacija važno je područje primarno orijentirano na ekstrakciju strukturiranih informacija iz nestrukturiranih tekstualnih izvora, koje svoje metode i pristupe crpi iz obrade prirodnog jezika i umjetne inteligencije. Jedan od standardnih izvora podataka su članci (online) novinskih portala. Standardni pristupi ekstrakcije članaka s novinskih portala temeljeni su na ručno pisanim pravilima i heuristikama. Ovaj diplomski rad istražuje mogućnosti kombiniranja tradicionalnih algoritama zajedno s računalnim vidom za poboljšavanje ekstrakcije članaka s novinskih portala.Rapid adoption of new mobile technologies, such as smartphones and tablets, enabled continuous and uninterrupted communication worldwide. Communicating through various different mediums, such as text and images, continuously creates data. That data needs to be processed and analyzed. Information extraction, an important field for extracting structured information from raw, unstructured textual sources, draws its methods from natural language processing and artificial intelligence. In the domain of information extraction is extracting articles from online news portals. Standard approaches use various heuristics and hand-crafted rules. This thesis explores the combination of computer vision and traditional approaches for improving the results of article extraction

Object Tracking Using Camera Mounted on Mobile Robot

.

Extraction of Articles from News Portals Using Machine Learning

Revolucijom novih tehnologija, primarno pametnih mobilnih uređaja, omogućena je konstantna i neprekidna komunikacija. Neprekidna komunikacija različitom vrstom medija, poput teksta i slika, stvara neprekidan tok podataka. Ti se podaci trebaju obraditi. Ekstrakcija informacija važno je područje primarno orijentirano na ekstrakciju strukturiranih informacija iz nestrukturiranih tekstualnih izvora, koje svoje metode i pristupe crpi iz obrade prirodnog jezika i umjetne inteligencije. Jedan od standardnih izvora podataka su članci (online) novinskih portala. Standardni pristupi ekstrakcije članaka s novinskih portala temeljeni su na ručno pisanim pravilima i heuristikama. Ovaj diplomski rad istražuje mogućnosti kombiniranja tradicionalnih algoritama zajedno s računalnim vidom za poboljšavanje ekstrakcije članaka s novinskih portala.Rapid adoption of new mobile technologies, such as smartphones and tablets, enabled continuous and uninterrupted communication worldwide. Communicating through various different mediums, such as text and images, continuously creates data. That data needs to be processed and analyzed. Information extraction, an important field for extracting structured information from raw, unstructured textual sources, draws its methods from natural language processing and artificial intelligence. In the domain of information extraction is extracting articles from online news portals. Standard approaches use various heuristics and hand-crafted rules. This thesis explores the combination of computer vision and traditional approaches for improving the results of article extraction

Object Tracking Using Camera Mounted on Mobile Robot

.

Extraction of Articles from News Portals Using Machine Learning

Revolucijom novih tehnologija, primarno pametnih mobilnih uređaja, omogućena je konstantna i neprekidna komunikacija. Neprekidna komunikacija različitom vrstom medija, poput teksta i slika, stvara neprekidan tok podataka. Ti se podaci trebaju obraditi. Ekstrakcija informacija važno je područje primarno orijentirano na ekstrakciju strukturiranih informacija iz nestrukturiranih tekstualnih izvora, koje svoje metode i pristupe crpi iz obrade prirodnog jezika i umjetne inteligencije. Jedan od standardnih izvora podataka su članci (online) novinskih portala. Standardni pristupi ekstrakcije članaka s novinskih portala temeljeni su na ručno pisanim pravilima i heuristikama. Ovaj diplomski rad istražuje mogućnosti kombiniranja tradicionalnih algoritama zajedno s računalnim vidom za poboljšavanje ekstrakcije članaka s novinskih portala.Rapid adoption of new mobile technologies, such as smartphones and tablets, enabled continuous and uninterrupted communication worldwide. Communicating through various different mediums, such as text and images, continuously creates data. That data needs to be processed and analyzed. Information extraction, an important field for extracting structured information from raw, unstructured textual sources, draws its methods from natural language processing and artificial intelligence. In the domain of information extraction is extracting articles from online news portals. Standard approaches use various heuristics and hand-crafted rules. This thesis explores the combination of computer vision and traditional approaches for improving the results of article extraction