One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Table 4: WSD single-classifier BLSTM with other pieces or hyper-parameters
['[BOLD] Network (Our Single-classifier)', '[BOLD] F-measure(%)']
The first two algorithms represent the state-of-the-art models of supervised WSD when evaluated on SensEval-3. While the lower layers of these networks are shared, upper layers of each network are responsible to individually classify the ambiguous that word the network is associated with. IMS+adapted is another WSD model that considers deep neural networks and also uses pre-trained word embeddings as inputs. In contrast to Multi-classifier BLSTM, this model relies on features such as POS tags, collocations, and surrounding words to achieve their result. For these two models, softmax constitutes the output layers of all networks. htsa3 It is a Naive Bayes system applied mainly to raw words, lemmas, and POS tags with correction of the a-priori frequencies. Considering their ranking scores we see that there are unsupervised methods that outperform these supervised algorithms. Within-our-model comparisons - Besides several internal experiments to examine the importance of some hyper-parameters to our network, we investigated if the sequential follow of cosine similarities computed between a true sense and its preceding and succeeding context words carries a pattern-like information that can be encoded with BLSTM.