Scalable Approach for Normalizing E -commerce Text Attributes ( SANTA ) (2021)

Abstract: In this paper, we present SANTA, a scalable framework to automatically normalize E-commerce attribute values (e.g. “Win 10 Pro”) to a fixed set of pre-defined canonical values (e.g. “Windows 10”). Earlier works on attribute normalization focused on fuzzy string matching (also referred as syntactic matching in this paper). In this work, we first perform an extensive study of nine syntactic matching algorithms and establish that ‘cosine’ similarity leads to best results, showing 2.7% improvement over commonly used Jaccard index. Next, we show that string similarity alone is not sufficient for attribute normalization as many surface forms require going beyond syntactic matching (e.g. “720p” and “HD” are synonyms). While semantic techniques like unsupervised embeddings (e.g. word2vec/fastText) have shown good results in word similarity tasks, we observed that they perform poorly to distinguish between close canonical forms, as these close forms often occur in similar contexts. We propose to learn token embeddings using a twin network with triplet loss. We propose an embedding learning task leveraging raw attribute values and product titles to learn these embeddings in a self-supervised fashion. We show that providing supervision using our proposed task improves over both syntactic and unsupervised embeddings based techniques for attribute normalization. Experiments on a real-world dataset of 50 attributes show that the embeddings trained using our proposed approach obtain 2.3% improvement over best string similarity and 19.3% improvement over best unsupervised embeddings.

## overview attribute normalization can be posed as a matching problem. given an attribute surface form and a list of possible canonical forms, similarity of surface form with each canonical form is calculated and surface form is mapped to the canonical form with highest similarity or mapped to 'other' class if none of the canonical forms is suitable (refer figure 2 for illustration). formally, given a surface form s i (@xmath0, n]) and a list of canonical forms c j (@xmath1, k]), where c 0 is the 'other' class, n is number of surface forms and k is number of canonical forms. the aim is to find a mapping function m such that: in this paper, we explore fuzzy string matching and similarity in embedding space as matching figure 3: santa framework for training embeddings suitable for attribute normalization techniques. we describe multiple string matching approaches in section 3.1, followed by unsupervised token embedding approaches in section 3.2 and our proposed santa framework in section 3.3. ## experimental setup in this section, we describe our experimental setup, including dataset, metrics and hyperparameters of our model. there is no publicly available data set for attribute normalization problem. more (2016) and putthividhya and hu (2011) worked on brand normalization problem but the datasets are not published for reuse. @xcite published a dataset collected from aliexpress 'sports & entertainment' category for attribute extraction use-case. this dataset belongs to a single category and is restricted to samples where attribute value is present in title, hence limiting its applicability for attribute normalization. to ensure robust learnings, we curate a real-world attribute normalization dataset spread across multiple categories and report all our evaluations on this dataset. ## results we present systematic study on string similarity approaches in section 5.1, followed by experiments of unsupervised embeddings in section 5.2. we compare best results from section 5.1 and section 5.2 with our proposed santa framework in section 5.3. we study these algorithms separately on syntactic and semantic portion of test dataset in section 5.4 and perform qualitative analysis based on t-sne visualization in section 5.5. ## conclusion in this paper, we studied the problem of attribute normalization for e-commerce. we did a systematic study of multiple syntactic matching algorithms and established that use of 'cosine similarity' leads to 2.7% improvement over commonly used jaccard index. additionally, we argued that attribute normalization requires combination of syntactic and semantic matching. we described our santa framework for attribute normalization, including our proposed task to learn embeddings in a self-supervised fashion with twin network and triplet loss. evaluation on a real-world dataset for 50 attributes, shows that embeddings learnt using our proposed santa framework outperforms best string matching algorithm by 2.3% and fasttext by 19.3% for attribute normalization task. our evaluation based on semantic and syntactic examples and t-sne plots provide useful insights into qualitative behaviour of these embeddings.