Pregled bibliografske jedinice broj: 1124429
Takelab at SemEval-2017 task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Takelab at SemEval-2017 task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news // Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Vancouver, Kanada, 2017. str. 866-871 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Takelab at SemEval-2017 task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Autori
Rotim, Leon ; Tutek, Martin ; Šnajder, Jan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
/ - , 2017, 866-871
Skup
The 11th International Workshop on Semantic Evaluation (SemEval-2017)
Mjesto i datum
Vancouver, Kanada, 03.08.2017. - 04.08.2017
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
sentiment analysis ; kernel regression ; support vector machine
Sažetak
This paper describes our system for fine-grained sentiment scoring of news headlines submitted to SemEval 2017 task 5–subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word vectors as features. Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0.733.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
