SlowGuess/NAACL_2022_OCR huggingface dataset:
where  $c^{l-1}$  is a learnable parameter to approximate the importance of node v towards u (attv→u) based on their interaction in the latent space in  $l$  layer-wise manner,  $W$  is the layer-wise projection matrix, and  $\oplus$  denotes concatenation.
Table 1 shows the basic statistics of the datasets that we consider in this study. The taxonomy networks are extracted from Assamese, Bengali, and Hindi WordNets, respectively. The Assamese WordNet dataset consists of 8466 of noun concepts and 8463 edges. The network has the maximum in-degree path of 1, implying that each concept node has only one parent node or predecessor. The network consists of 7072 leaf nodes. Bengali WordNet dataset contains 26007 of noun concepts and 25815 edges and has the maximum in-degree path of 1. The network consists of 7072 leaf nodes. Hindi WordNet dataset consists of 28242 of noun concepts and 28016 edges. It contains 24737 leaf nodes.
D Ranking and Classification Metrics
We use an array of performance metrics from the domain of classification and ranking to evaluate the competing methods' performances. Among the ranking metrics, we use Mean Rank (MR), Hit@k  $(\mathrm{k} = 1,3)$ , and Mean Reciprocal Rank (MRR) to judge how well a competing method performs in producing a ranked list of candidate anchors given a test query and a taxonomy expansion operation to carry-out - merge or attach.
- Mean Rank: It calculates the average rank of true anchors among all the candidate anchors with respect to the matching scores, given a query.
- Hit@k: It calculates the number of times a true anchor appears in the top k positions when matched with a test query.
- Mean Reciprocal Rank(MRR): The Mean Reciprocal Rank is used to assess the ranking quality of the true anchor. The reciprocal rank can be computed by finding and inversing the
rank of a true anchor in the predicted anchors' list of each query. MRR is averaged over all queries.
Further, we use Accuracy, Micro/ Macro F1, Precision, Recall, and F-Scores as classification metrics for deciding given a test query and an initial taxonomy tree, which operation among merge (M), attach (A), and no-operation (N) is to be performed.
- Accuracy: It summarizes the performance of the classification model as the fraction of the number of true tasks predicted over the total number of ground-truth tasks for a set of queries.
- Precision: It calculates the fraction of true-positive predicted expansion task classes among the total number of true-positive and false-negative task classes.
- Recall: It calculates the fraction of true-positive predicted expansion task classes among all the relevant ground-truth task classes.
- F-Score: The harmonic mean of precision and recall. It is also known as F1-Score.
- Micro/ Macro F1: The Macro F1 computes F1-Score for each class (merge M/ attach A) independently but averages the final score by treating each expansion task-class as equally contributing. However, Micro F1 computes the F1-Score for each query sample in the training set and therefore aggregates the contributions of all expansion task classes to compute the final average metric.
E Evaluation Strategy
We obtain the initial feature vector for train and test concepts using pre-trained subword-aware Fast-text embeddings. For each concept, we generate its definition embedding by averaging the embedding of each word in its textual definition. We employ PyTorch and DGL framework  $^{3}$  to load and train embeddings. In TEAM, we use a two-layer position-enhanced GAT where the first layer (of size 300) has four attention heads and the second layer (of size 600) has one attention head. We use 50-dimension position embeddings for both layers and apply dropout with the rate of 0.1 on the input
<sup>3</sup>https://github.com/dmlc/dgl
feature vectors. We use Adam optimizer with an initial learning rate of 0.001.
F Ranking result for out-of-vocabulary words
In the case of attach expansion, TEAM-RG beats state-of-the-art Taxo-expan by a large margin of (321, 0.31, 0.48, 0.69) in MR, Hit@1, Hit@3, MRR, respectively. However, our proposed frameworks are seen not to perform so well in merge M operation as compared to the attach A operation. Intuitively, this is because, for OOV words, we use a set of manually collected paraphrase definitions of the OOV words to match them with the candidate anchor concepts in the existing taxonomy. Whereas for actually training our model, we have used the same definitions in the replica nodes. That is, we have used the same definition in the original anchor concept and in the input query-concept with mutually exclusive synset information. Thus, in this case-study, the paraphrase-based definition matching deems challenging for our learning model resulting in poorer results for M operation. We believe we can always eliminate this drawback by using a description generation tool (Wang et al., 2021) to generate different definitions of the same concept nodes and train our learning model in a more powerful way.