SlowGuess/ACL_2020_OCR huggingface dataset:
In addition, we compare BNSL with recurrent neural networks (RNNs). We apply attention-based Bi-LSTM (Zhou et al., 2016) and derive three versions of RNNs as baseline methods, RNN(f), RNNSEN) and RNN(e). RNN(f) determines the concepts of an entity according to the facts containing the entity, while RNNSEN) by the sentences containing the co-appearance of an entity and a concept. Specifically, each entity in RNN(f) is represented by its associated facts. Each fact is a sequence of subject, predict and object. Each subject, predict and object vector is fed in sequence into RNN(f), resulting a fact embedding vector. The averaged fact vector becomes the entitys feature for concept classification.
Similar to HypeNet, RNNSEN) requires the entity-concept pairs co-appearing in sentences. Dif
1http://reverb.cs.washington.edu 2https://concept.research.microsoft. com/Home/Download 3https://www.kaggle.com/mikeortman/   
wikipedia-sentences 4https://ai.baidu.com/broad/download? dataset  $\equiv$  saoke
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ferent from RNNSEN), RNN(e) focuses on sentences containing the entity only. Based on the sentences, RNN(e) aims to learn which concept an entity belongs to. We follow HypeNet and RNN to use pre-trained GloVe embeddings (Pennington et al., 2014) for initialization. Besides, we compare BNSL with traditional support vector machines (SVM) with linear kernel. The input features for SVM and BNSL are the same, i.e., the top  $K$  relations for each concept. Here we set  $K = 5$ .
During testing, all methods are evaluated on the same testing entities. we calculate the accuracy, precision, recall and F1-score over the prediction results for evaluation. We split the data into  $80\%$  of training and  $20\%$  of testing. For English, the total numbers of training and testing data are 504,731 and 123,880, respectively; whereas for Chinese, the numbers are 5,169,220 and 1,289,382, respectively.
In this section, we show the evaluation performance on the task of concept discovery with the learned interpretable relationships from open domain fact. Table 3 and Table 4 list the results for co-occurred and non co-occurred entity-concept pairs in sentences respectively. In the tables, (s) and (o) mean the performance only under the subject and the object view, respectively. RNN(f), BNSL and SVM present the prediction performance with the concatenation of both the subject and object views. As is mentioned in the previous section, we can use TF or TFIDF for the most relevant relation selection. We test both strategies for BNSL and SVM. For the English dataset, TFIDF performs much better than TF while the result is the opposite for the Chinese dataset. In this section, we analyze the results of BNSL and SVM with TFIDF for the English dataset. For the Chinese dataset, we report the performance of BNSL and SVM with TF. We will show more results for the relation selection in the next section.
For the co-occurred entity-concept pairs in sentences, BNSL(s) performs the best for both datasets. Surprisingly, SVM performs much better than HyperNet with an improvement of around  $10\%$  on accuracy for both datasets as is shown in Table 3. In addition, SVM achieves better results compared to RNNSEN). The reason that HypeNet or RNNSEN cannot perform well may be that the information expressed from the sentences are too diverse. HyperNet or RNNSEN cannot capture meaningful pat
terns from sentences for the task of concept discovery. Since RNN(e) further ignores the concept information during the sentence collection step, it cannot perform well compared with RNNSEN). In contrast, information extracted from open domain facts are much more concentrated about concepts. Furthermore, the most relevant relations associated with entities help filtering out noise. Therefore, SVM can achieve a much better result than sentence-based baselines.
Though SVM does well on the co-occurred data, BNSL outperforms SVM with all the four evaluation metrics. By learning interpretable relationships between relations and concepts, BNSL captures the most important knowledge about concepts and further exploits their dependencies to help improve the concept discovery task. However, the concatenation of subject and object views for BNSL cannot help improve the performance for both datasets. Similar phenomena can be observed for RNN(f) and SVM. Specifically, the results under the subject view are usually better than those of the object view, implying that when people narrate facts, they may pay more attention to selecting suitable predicate for subjects, rather for objects. Table 4 lists the performances of RNN(e), RNN(f), SVM and BNSL on non co-occurred data. We can observe a similar trend compared to the results on co-occurred data.
Since HypeNet and BNSL make use of different information sources (natural language sentences for HypeNet and open domain facts for BNSL), we try to ensemble them to improve the performance further. We first train HypeNet and BNSL independently. Then we can obtain prediction probabilities of entity-concept pairs from HypeNet and BNSL separately. We select the probabilities with higher values as the final predictions. The last row in Table 3 shows the performance of ensembling HypeNet and BNSL. We denote it as  $\mathrm{B} + \mathrm{H}$ . It can be seen that  $\mathrm{B} + \mathrm{H}$  achieves the best accuracy, recall and F1-scores on the co-occurred data. It reveals that interpretable relationships extracted from open domain facts are complementary to natural language sentences in helping concept discovery. Studying meaningful knowledge from open domain facts provides an alternative perspective to build concept graphs and this paper starts the first trial.
4.4 Analysis on the Relation Selection
Relation selection helps reducing the complexity of BNSL. In this section, we first evaluate how differ
Dataset	English	Chinese
Method	Accuracy	Precision	Recall	F1-score	Accuracy	Precision	Recall	F1-score
HypeNet	69.64%	75.09%	69.74%	72.31%	76.57%	87.17%	71.22%	78.39%
RNNSEN)	77.18%	80.74%	78.62%	79.67%	71.90%	72.85%	84.35%	78.18%
RNN(e)	67.77%	77.09%	61.62%	68.49%	57.67%	61.19%	79.53%	69.16%
RNN(s)	73.38%	80.35%	70.39%	75.04%	64.93%	64.02%	94.13%	76.21%
RNN(o)	70.95%	79.81%	65.46%	71.93%	64.97%	64.08%	94.01%	76.21%
RNN(f)	70.01%	79.08%	64.25%	70.90%	49.55%	61.23%	42.81%	49.95%
SVM(s)	76.68%	74.82%	88.93%	81.26%	85.06%	90.01%	84.33%	87.07%
SVM(o)	74.81%	72.72%	89.14%	80.10%	51.86%	57.54%	73.87%	64.69%
SVM	77.43%	74.38%	92.00%	82.25%	86.07%	90.86%	85.22%	87.95%
BNSL(s)	86.03%	82.89%	95.07%	88.56%	87.54%	92.40%	86.21%	89.20%
BNSL(o)	86.22%	84.52%	92.76%	88.45%	49.03%	56.79%	61.10%	58.86%
BNSL	84.79%	81.87%	94.08%	87.55%	87.37%	92.32%	86.00%	89.05%
B + H	91.27%	91.15%	93.75%	92.43%	87.88%	86.01%	95.18%	90.36%
Table 3: Performance on the co-occurred data. The best results are in bold.
Dataset	English	Chinese
Method	Accuracy	Precision	Recall	F1-score	Accuracy	Precision	Recall	F1-score
RNN(e)	63.94%	67.38%	52.09%	58.75%	53.82%	51.84%	95.06%	67.09%
RNN(s)	73.83%	74.61%	71.12%	72.82%	55.18%	52.55%	97.49%	68.29%
RNN(o)	73.74%	77.05%	66.56%	71.42%	55.34%	52.64%	97.47%	68.36%
RNN(f)	72.36%	75.53%	65.02%	69.88%	51.82%	51.63%	42.45%	46.59%
SVM(s)	71.94%	66.48%	86.91%	75.34%	90.03%	86.73%	94.30%	90.36%
SVM(o)	65.82%	61.55%	81.70%	70.21%	51.14%	50.39%	85.37%	63.37%
SVM	71.62%	65.62%	89.16%	75.60%	90.91%	88.11%	94.37%	91.14%
BNSL(s)	85.97%	82.15%	91.42%	86.54%	92.47%	90.12%	95.23%	92.60%
BNSL(o)	82.27%	78.36%	88.48%	83.11%	51.52%	50.70%	74.63%	60.38%
BNSL	84.78%	80.77%	90.74%	85.47%	92.39%	90.05%	95.15%	92.53%
Table 4: Performance on the non co-occurred data. The best results are in bold.
ent relation selection strategies will influence the performance of BNSL and SVM methods. Table 5 is the performance of TF and TFIDF relation selection on the entire data for both English and Chinese. We observe that TFIDF selection performs better on English while TF is better on Chinese. However, BNSL always outperforms SVM regardless of the views or the relation selections. In addition, since SVM performs much better than the neural network based HypeNet and RNN, we try to ensemble it with BNSL to improve the performance further. We consider the prediction probabilities of SVM as a new variable and incorporate it into BNSL for network structure learning. We denote the model as  $\mathrm{BNSL +}$  SVM. For comparison, we ensemble SVM with BNSL by taking the results of BNSL as one new feature dimension to SVM. We name it as  $\mathrm{SVM +}$  BNSL. It can be seen from Table 5 that the ensemble of BNSL and SVM outperforms single models on both datasets. Especially,  $\mathrm{BNSL +}$
SVM does better than SVM + BNSL, revealing that BNSL has a better capability of exploring meaningful knowledge from other sources.
Furthermore, we evaluate how BNSL performs with different numbers of relations. Figure 3 shows the results of BNSL(s) by setting relation numbers from 1 to 20. TFIDF relation selection is used for the English dataset and TF for Chinese. We can observe that BNSL performs best when we select the top 5 relations and the results become stable with more than 5 relations.
Figure 3: BNSL(s) with different numbers of relations.
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Relation Selection	TF Selection	TFIDF Selection
Dataset	Method	Accuracy	Precision	Recall	F1-score	Accuracy	Precision	Recall	F1-score
English	SVM(s)	58.19% (10)	55.17% (10)	87.43% (6)	67.65% (11)	72.38% (10)	67.28% (10)	87.12% (10)	75.93% (11)
BNSL(s)	71.57% (5)	67.93% (5)	81.70% (10)	74.19% (6)	86.00% (2)	82.24% (2)	91.82% (2)	86.77% (2)
SVM + BNSL(s)	71.62% (4)	68.36% (4)	80.48% (11)	73.93% (7)	82.04% (7)	78.31% (6)	88.63% (7)	83.15% (7)
BNSL + SVM(s)	78.46% (1)	80.55% (1)	75.04% (12)	77.70% (3)	88.36% (1)	86.48% (1)	90.94% (4)	88.65% (1)
SVM(o)	55.07% (12)	52.91% (12)	92.29% (1)	67.26% (12)	66.65% (12)	62.64% (12)	82.48% (12)	71.21% (12)
BNSL(o)	71.14% (7)	65.68% (7)	88.54% (5)	75.42% (4)	82.64% (5)	78.99% (5)	88.95% (6)	83.67% (6)
SVM + BNSL(o)	66.84% (9)	61.65% (9)	89.07% (3)	72.87% (8)	78.27% (9)	74.79% (8)	85.28% (11)	79.70% (9)
BNSL + SVM(o)	77.02% (2)	73.10% (2)	85.50% (7)	78.81% (1)	84.16% (4)	81.49% (3)	88.40% (9)	84.80% (4)
SVM	57.38% (11)	54.36% (11)	92.05% (2)	68.35% (10)	72.15% (11)	66.46% (11)	89.45% (5)	76.26% (10)
BNSL	71.26% (6)	66.77% (6)	84.63% (9)	74.65% (5)	84.78% (3)	80.89% (4)	91.09% (3)	85.69% (3)
SVM + BNSL	68.31% (8)	63.71% (8)	85.09% (8)	72.86% (9)	78.70% (8)	73.99% (9)	88.50% (8)	80.60% (8)
BNSL + SVM	75.84% (3)	70.60% (3)	88.58% (4)	78.57% (2)	82.22% (6)	76.50% (7)	93.03% (1)	83.96% (5)
Chinese	SVM(s)	89.80% (8)	86.91% (8)	93.73% (5)	90.19% (8)	74.58% (8)	67.98% (6)	92.95% (8)	78.53% (8)
BNSL(s)	92.23% (5)	90.24% (5)	94.71% (1)	92.42% (5)	75.01% (6)	67.90% (8)	94.88% (1)	79.16% (6)
SVM + BNSL(s)	93.31% (4)	93.13% (4)	93.52% (8)	93.32% (4)	76.37% (3)	69.62% (3)	93.55% (6)	79.83% (3)
BNSL + SVM(s)	95.56% (1)	97.36% (1)	93.65% (7)	95.47% (1)	77.54% (2)	70.64% (2)	94.27% (4)	80.76% (2)
SVM(o)	51.16% (12)	50.71% (12)	82.58% (9)	62.84% (10)	50.55% (12)	50.33% (12)	84.65% (10)	63.12% (10)
BNSL(o)	51.39% (10)	50.96% (10)	73.85% (11)	60.31% (12)	50.79% (10)	50.55% (10)	72.37% (12)	59.53% (12)
SVM + BNSL(o)	51.33% (11)	50.82% (11)	82.41% (10)	62.87% (9)	50.66% (11)	50.39% (11)	84.73% (9)	63.20% (9)
BNSL + SVM(o)	51.72% (9)	51.18% (9)	74.54% (12)	60.69% (11)	50.97% (9)	50.68% (9)	72.98% (11)	59.82% (11)
SVM	90.35% (7)	87.69% (7)	93.88% (4)	90.68% (7)	74.68% (7)	67.95% (7)	93.45% (7)	78.68% (7)
BNSL	92.15% (6)	90.16% (6)	94.62% (2)	92.34% (6)	75.12% (5)	68.08% (5)	94.61% (2)	79.18% (5)
SVM + BNSL	93.61% (3)	93.55% (3)	93.68% (6)	93.61% (3)	76.33% (4)	69.57% (4)	93.60% (5)	79.82% (4)
BNSL + SVM	95.46% (2)	96.59% (2)	94.25% (3)	95.40% (2)	77.68% (1)	70.77% (1)	94.32% (3)	80.87% (1)
Table 5: Performance of relation selections on the entire data. The results are reported as “value + (rank)”.
4.5 Analysis with missing information
In reality, the open domain facts or co-occurring sentences associated with entity-concept pairs are usually missing, making the input information for concept discovery extremely sparse. In this section, we study how BNSL performs with the sparse input. Given a set of entities, we first extract the corresponding facts (or sentences) under each concept. For both datasets, we get around 30 million entity-concept pairs for testing and more than  $97\%$  do not have the corresponding fact information with the top  $K$  relations, making the prediction of BNSL very challenging. Furthermore, both datasets have a large number of fine-grained concepts, making the task more difficult. For the missing data, we feed an empty fact or sentence into BNSL and other models for training and testing. Also, we observe that RNN does not perform as well compared with other methods and in particular RNNSEN) performs the worst when the input is extremely sparse.
In Figure 4, we report the improvement of F1-score over RNNSEN). We can observe that HyperNet, SVM and BNSL can achieve much better performance, showing their robustness with missing values. In addition,  $\mathrm{B} + \mathrm{H}$  can still achieve the best result. It further confirms that open domain facts and natural language sentences are comple
Figure 4: F1-score improvement on RNNSEN).
mentary to each other even when there is a large portion of missing information.
In this paper, we investigate the task of learning interpretable relationships between entities, relations and concepts from open domain facts to help enriching and refining concept graphs. The Bayesian network structures are learned from open domain facts as the discovered meaningful dependencies between relations of facts and concepts of entities. Experimental results on an English dataset and a Chinese dataset reveal that the learned network structures can better identify concepts for entities based on the relations of entities from open domain facts, which will further help building a more complete concept graph.
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