Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Golshan, Behzad"'
Creating and collecting labeled data is one of the major bottlenecks in machine learning pipelines and the emergence of automated feature generation techniques such as deep learning, which typically requires a lot of training data, has further exacer
Externí odkaz:
http://arxiv.org/abs/2005.06133
Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect
Externí odkaz:
http://arxiv.org/abs/2004.14283
Autor:
Traylor, Aaron, Chen, Chen, Golshan, Behzad, Wang, Xiaolan, Li, Yuliang, Suhara, Yoshihiko, Li, Jinfeng, Demiralp, Cagatay, Tan, Wang-Chiew
Review comprehension has played an increasingly important role in improving the quality of online services and products and commonsense knowledge can further enhance review comprehension. However, existing general-purpose commonsense knowledge bases
Externí odkaz:
http://arxiv.org/abs/2004.03020
Paraphrases are important linguistic resources for a wide variety of NLP applications. Many techniques for automatic paraphrase mining from general corpora have been proposed. While these techniques are successful at discovering generic paraphrases,
Externí odkaz:
http://arxiv.org/abs/1910.00637
We present Emu, a system that semantically enhances multilingual sentence embeddings. Our framework fine-tunes pre-trained multilingual sentence embeddings using two main components: a semantic classifier and a language discriminator. The semantic cl
Externí odkaz:
http://arxiv.org/abs/1909.06731
In recent years, the proliferation of online resumes and the need to evaluate large populations of candidates for on-site and virtual teams have led to a growing interest in automated team-formation. Given a large pool of candidates, the general prob
Externí odkaz:
http://arxiv.org/abs/1811.05015
Autor:
Wang, Xiaolan, Feng, Aaron, Golshan, Behzad, Halevy, Alon, Mihaila, George, Oiwa, Hidekazu, Tan, Wang-Chiew
We present the KOKO system that takes declarative information extraction to a new level by incorporating advances in natural language processing techniques in its extraction language. KOKO is novel in that its extraction language simultaneously suppo
Externí odkaz:
http://arxiv.org/abs/1805.01083
Autor:
Asai, Akari, Evensen, Sara, Golshan, Behzad, Halevy, Alon, Li, Vivian, Lopatenko, Andrei, Stepanov, Daniela, Suhara, Yoshihiko, Tan, Wang-Chiew, Xu, Yinzhan
The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion. Recently, there has been interest in developing technologies that help incorporate the findings of the
Externí odkaz:
http://arxiv.org/abs/1801.07746
Motivated by applications that arise in online social media and collaboration networks, there has been a lot of work on community-search and team-formation problems. In the former class of problems, the goal is to find a subgraph that satisfies a cer
Externí odkaz:
http://arxiv.org/abs/1701.05352
Publikováno v:
In Expert Systems With Applications 1 April 2019 119:441-455
