Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
Table 4: Results of using different resources. The seq2seq model applies the basic architecture without adaptation to the multi-label problem. SingleRes indicates that the encoder only considers a single textual resource. MultiRes indicates that the encoder considers multiple textual resources (Wiki and Dictionary).
['Model', 'Precision', 'Recall', 'F1']
Effect of Applying Multi-Resource: This corresponds with our expectation as more descriptions can provide more comprehensive information of the word from various aspects. Moreover, since the alignment between sememes and descriptions are noisy, the gate mechanism can automatically decide how much one description contributes to the prediction based on its relatedness. Between the two resources we used (Baidu Wiki and Baidu Dictionary), dictionary-style resource provides much higher precision (47.15 ∼ 42.91), we assume this is because the descriptions in this kind of resource have better quality in general. However, many new words and rare words are not included in the dictionary and some of the entries in the Baidu Dictionary have noisy descriptions as well (e.g., English descriptions instead of Chinese), so dictionary alone does not predict as well as the multi-resource one.