O pen B rand: Open Brand Value Extraction from Product Descriptions (2022)

Abstract: Extracting attribute-value information from unstructured product descriptions continue to be of a vital importance in e-commerce applications. One of the most important product attributes is the brand which highly influences costumers’ purchasing behaviour. Thus, it is crucial to accurately extract brand information dealing with the main challenge of discovering new brand names. Under the open world assumption, several approaches have adopted deep learning models to extract attribute-values using sequence tagging paradigm. However, they did not employ finer grained data representations such as character level embeddings which improve generalizability. In this paper, we introduce OpenBrand, a novel approach for discovering brand names. OpenBrand is a BiLSTM-CRF-Attention model with embeddings at different granularities. Such embeddings are learned using CNN and LSTM architectures to provide more accurate representations. We further propose a new dataset for brand value extraction, with a very challenging task on zero-shot extraction. We have tested our approach, through extensive experiments, and shown that it outperforms state-of-the-art models in brand name discovery.

## introduction brand name plays a very important role in influencing customers' behaviour @xcite . typically, as customers are aware of the brand, they can deduce knowledge about other product attributes. let us take the example of the toy shown in figure 1 . the brand of this product is "gentle monster". by knowing the brand, customers would have some kind of associations, like this toy would be of "a soft and smooth wood", have "bright colors", and contain "small pieces which is suitable for older kids". so, when shopping for toys, they would pick a particular brand based on the attributes they find important. such correlations between brands and product attributes make it crucial for e-commerce applications to accurately extract brand names from product descriptions. retrieving brand names is addressed in the literature within the general problem of attribute-value extraction from product descriptions @xcite @xcite @xcite . early approaches rely on rule-based techniques which use domain-specific knowledge to identify attributes and values @xcite @xcite . such approaches adopt a closed world assumption requiring the possible set of values to be known beforehand by mean of dictionaries or hand-crafted rules. consequently, they are not suitable for discovering unseen values such as newly emerging brands. to tackle this problem, most recent approaches model the extraction task as sequence tagging @xcite @xcite and solve it using deep learning models such as bilstm enhanced by conditional random field (crf) and attention layers. these new approaches achieve promising results, however, they limit the representation of their data to word embeddings which can capture context but penalizes generalizability to new brands. in this paper, we propose to use character level embeddings in sequence tagging models for discovering brand names. in addition to word embeddings, character level embeddings were employed in named entity recognition (ner) tasks @xcite to handle out-of-vocabulary words. the problem of unseen words is particularly emphasized in brands because of sub-branding, brand fragmentation, or simply emerging businesses. unseen brand names can be completely new, like in brand fragmentation where new brands share the same parent brand maintaining minimal links between the new and the existing identities. for example, "audi" and "porsche" do not have any similarity although they have the same parent brand "volkswagen". by contrast, sub-branding would maintain stronger links between existing brands and the new generated ones, which can be reflected by similarities in brand names. examples include "uber" and "uberpool", "mcdonalds" and "mc-cafe", or "samsung" and "samsung evo". thus, the use of character level embedding is crucial for capturing variations in brand names and the occurrence of unseen brands. we summarize the main contributions of this work as follows: ## problem statement in this section, we formally define the problem of open brand value extraction. given a product title, represented as an unstructured text data, and a target attribute (eg. brand), our goal is to extract the appropriate values for the corresponding attribute from the product title. in this context, we want to discover new values that have not been encountered before. we formalize the attribute-value extraction as per the following definition: definition given a product title x. the title x is represented as a sequence of tokens x @xmath0, ..., x t }, where t is the sequence length. consider a target attribute a. attribute-value extraction automatically identifies a sub-sequence of tokens from x t as applicable attribute-value pair. for example, consider the title for the product given in the example of figure 1 : @xmath1, gentle monster wooden blocks toys for toddlers, colored building blocks -6 colors 2 dice." the tokenization of x yields: x @xmath2, x 2 , ..., x 25 } = {"wooden", "stacking", "board", .., "dice"}, where @xmath3. for the target attribute: @xmath4. we want to extract: @xmath5, x 13 } = {"gentle", "monster"}. in order to identify these sub-sequences, the sequence of tokens x t need to be tagged to capture sequential and positional information. for this purpose, we adopt the sequence tagging model and associate a tag from a given tag-set to the sequence of input tokens x t . we experimented with different tagging strategies and, inline with previous work in the literature @xcite , we found that the {b,i,o} tagging scheme produced the best results, where "b", "i", and "o" represent the beginning, inside, and outside of an attribute, respectively. (a sequence of "o" tags corresponds to the absence of an attribute). ## experimental setup this section presents the experimental settings of our empirical approach for comparing state-of-theart models on the task of brand value extraction. ## results and discussion we conducted a series of experiments on az-base, az-zero-shot, and az-new-cat datasets under various settings to evaluate the performance of open-brand. ## related work there has been significant research on the task of attribute-value extraction from product descriptions @xcite . initial approaches @xcite formulated the problem as a classification task relying on supervised learning techniques. @xcite use a naive bayes classifier to extract values that correspond to a predefined set of product attributes. (putthividhya and hu, 2011) focus on annotating brands in product listings of apparel products on ebay. @xcite propose a domain independent supervised system that can automatically discover product attributes from user reviews using wikipedia. similarly, @xcite propose an automatic labeling process of entities by making use of anchor links from wikipedia text. other approaches exploited unsupervised learning techniques like (shinzato and sekine, 2013) in their task of extracting attribute-values from e-commerce product pages. following a similar line, @xcite use consumer patterns to create annotations for datadriven products. @xcite focus on the discovery of hidden patterns in costumer reviews to improve attribute-value extraction. the above approaches provide promising results, however they poorly handle the discovery of new values due to their closed world assumption. the most recent approaches @xcite @xcite make instead an open world assumption using sequence tagging models, similarly to ner tasks @xcite . @xcite use a bilstm-crf model to tag several product attributes for brands and models with handcrafted features. @xcite develop an end-to-end tagging model utilizing bilstm and crf without using any dictionary or hand-crafted features. after that, @xcite adopted only one global set of bio tags for any attributes to scale up the semantic representation models of product titles. in this context, @xcite proposed a taxonomy aware knowledge extraction model that takes advantage of the hierarchical relationships between product categories. the latest approaches extend the open world assumption also to attributes and use question answering (qa) models @xcite to scale to a larger number of attributes. sequence tagging approaches are the most relevant to our work since extracting brand names does not require scalability. however, these models did not exploit character level embeddings which are crucial for improving generalizability. in our work, we enhance such models using different granularities of embeddings. ## conclusion in this paper we have addressed the problem of extracting brand values from product descriptions. previous state-of-the-art sequence tagging methods faced the challenge of discovering new values that have not been encountered before. to tackle this issue we proposed openbrand, a novel attribute-value extraction model with the integration of character level representations to improve generalizability. we presented experiments on realworld datasets in different categories which show that openbrand outperforms state-of-the-art approaches and baselines. by exploiting character level embeddings, openbrand is capable of learning accurate representations to discover new brand values. our experiments also show that cnn based representations outperform lstm based representations in both performance and computation. a natural extension of this work is to deal with the problem of disambiguation discussed in section 5.5. to this end, we need to have more training data which helps understating the patterns in a better way. moreover, we need to extend the tagging model to capture ambiguous product descriptions. this extension can be very important when brand values need to be extracted from other data sources other than concise product descriptions.