Таңбалар

We investigate the combination of several sources of information for the purpose of sub- jectivity recognition and polarity classification in meetings. We focus on features from two modalities, transcribed words …...

We describe a method to automatically extract social networks from literary texts. Similar to those in prior research, nodes represent characters found in the texts; edges connect them to other …...

This paper considers the problem of learning Chinese word embeddings. In contrast to En- glish, a Chinese word is usually composed of characters, and most of the characters them- selves …...

AbstractIn Neural Machine Translation (NMT) the usage of sub􏰃words and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words. However, …...

AbstractWe investigate subword information for Chinese word segmentation, by integrating sub word embeddings trained using byte-pair encoding into a Lattice LSTM (LaLSTM) network over a character sequence. Experiments on standard …...

AbstractApplying the Transformer architecture on the character level usually requires very deep architectures that are difficult and slow to train. These problems can be partially overcome by incorporating a segmentation …...

AbstractLanguage modelling and machine translation tasks mostly use subword or character inputs, but syllables are seldom used. Syllables provide shorter sequences than characters, require less-specialised extracting rules than morphemes, and …...