Neural Architectures for Fine-grained Entity Type Classification

Abstract

In this work, we investigate several neural network architectures for fine-grained entity type classification and make three key contributions. Despite being a natural comparison and addition, previous work on attentive neural architectures have not considered hand-crafted features and we combine these with learnt features and establish that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism learns to attend over syntactic heads and the phrase containing the mention, both of which are known to be strong hand-crafted features for our task. We introduce parameter sharing between labels through a hierarchical encoding method, that in lowdimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We demonstrate that the choice of training data has a drastic impact on performance, which decreases by as much as 9.85% loose micro F1 score for a previously proposed method. Despite this discrepancy, our best model achieves state-of-the-art results with 75.36% loose micro F1 score on the well-established FIGER (GOLD) dataset and we report the best results for models trained using publicly available data for the OntoNotes dataset with 64.93% loose micro F1 score. An illustration of the attentive encoder neural model predicting fine-grained semantic types for the mention "New Zealand" in the expression "a match series against New Zealand is held on Monday".

Entity Linking via Joint Encoding of Types, Descriptions, and Context

“…Note that we only learn the embeddings of each new entity, keeping all other Table 4: Typing Prediction: Performance on the FIGER (GOLD) dataset. Our performance is competitive with FIGER (Ling and Weld, 2012) and neural-LSTM model of Shimaoka et al (2017). Their SSIR-Full model that uses a biLSTM layer, an attention layer, combined with hand-crafted features is state-of-art for this task.…”

“…Compared to existing systems trained specifically for this task, embeddings from our approach (Model CDTE) performs competitively (see Table 4). In particular, our model performs better than the neural-LSTM model of Shimaoka et al (2017), suggesting that our multi-task linking, and typing loss facilitates effective encoding of mention contexts. Table 5: Example predictions by our models: Model CT (Ex.1) and CD (Ex.2) predict correctly when correct type prediction or background knowledge is sufficient, respectively.…”

End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification

“…Table 1 shows performance of PthDCode on test, based on the interval [40000, 50000]; average and standard deviation are computed for 2000(20 + i), 0 ≤ i ≤ 5, as described above. PthDCode achieves clearly better results than other baseline methods -FIGER (Ling and Weld, 2012), (Yogatama et al, 2015) and (Shimaoka et al, 2017) -when trained on raw (i.e., not denoised) datasets of a similar size. Attentive encoder (Shimaoka et al, 2017) is a neural baseline for PthDCode, to which comparison in Table 1 suggests decoding of path hierarchy rather than flat classification significantly improves the performance.…”

“…OOV vectors are randomly initialized. Similar to (Shimaoka et al, 2017), all hidden states h of the encoder-decoder were set to 100 dimension and mention lengths m to 5. Window size is w = 15.…”

Topic Information Based Neural Network Model for Fine-grained Entity Type Classification

“…[12] [14] first presented a method without using feature engineering and external resources. Shimaoka et al (2016) [15] then incorporated an attention mechanism to allow the model to focus on relevant expressions.…”