AVEN - GR : Attribute Value Extraction and Normalization using product GR aphs (2023)

Abstract: Getting a good understanding of the user intent is vital for e-commerce applications to surface the right product to a given customer query. Query Understanding (QU) systems are essential for this purpose, and many e-commerce providers are working on complex solutions that need to be data efficient and able to capture early emerging market trends. Query Attribute Understanding (QAU) is a sub-component of QU that involves extracting named attributes from user queries and linking them to existing e-commerce entities such as brand, material, color, etc. While extracting named entities from text has been extensively explored in the literature, QAU requires specific attention due to the nature of the queries, which are often short, noisy, ambiguous, and constantly evolving. This paper makes three contributions to QAU. First, we propose a novel end-to-end approach that jointly solves Named Entity Recognition (NER) and Entity Linking (NEL) and enables open-world reasoning for QAU. Second, we introduce a novel method for utilizing product graphs to enhance the representation of query entities. Finally, we present a new dataset constructed from public sources that can be used to evaluate the performance of future QAU systems.

## introduction search queries are the main point of interaction between the customer and the search system. as such, extracting information from the queries is pivotal in surfacing the relevant products, making the task directly responsible for the quality of the overall customer experience. query understanding (qu) not only inherits all the challenges of standard natural language understanding but poses additional difficulties: queries are short and lack context, which makes them challenging to understand. they often contain implicit knowledge that * these authors contributed equally to this work is difficult to capture without external reference. for example, the query "m2 laptop" refers to apple laptops since m2 processors are only sold by apple. furthermore, customers do not have technical writing skills, which can result in queries that are noisy or use inappropriate search terms. in this work, we focus on the task of query attribute understanding (qau), which aims to extract the attribute values from the queries and make them usable for other downstream applications in the search engine (see fig. 1 ). qau is related to another important task, document attribute understanding (dau), which aims to extract attributes from product descriptions. dau has received significant attention from the community in the past years @xcite @xcite ) and does not suffer from the difficulties mentioned above and that are specific to queries. both qau and dau are specific instances of named entity recognition and linking (ner/nel), which aims to extract typed mentions from text. however, in contrast to classic ner, which usually handles fewer attribute types (such as person, location, and organization), qau and dau deal with a larger number of attribute types (which can reach thousands in e-commerce as noted in @xcite ). we claim that three critical elements need to be addressed to get a practical solution to qau. firstly, named entity recognition should be performed jointly with entity linking, in order to map the detected entities to our knowledge base. solving these tasks separately is not practical in an industrial context, as it leads to error propagation (linking module cannot make up for a wrong attribute prediction by the ner module) and more generally hidden technical debt (see @xcite ). furthermore, separating the tasks precludes the possibility of inductive transfer, which has been shown to be crucial in related tasks @xcite @xcite . figure 1 : overview of the task. we ultimately want to detect that this query contains three mentions: (brown chocolate), (boot) and (suede). the first annotation row shows the ground truth for the attribute value extraction task, while the second one shows that of the normalization step, which may be understood as entity linking over the detected mentions. secondly, product graphs (pg) are becoming a new standard to represent e-commerce concepts and the relations between searchable products. therefore, qau systems should be able to leverage this new source of knowledge to improve their performance. finally, qau systems should always be designed with an "open-world" setup in mind to deal dynamically with new concepts. for instance, if we consider the query 'sony a95k tv', we should be able to detect that 'a95k' is a mention representing a product line even if this product does not exist in our knowledge base. note that extreme classification @xcite ) is a possible alternative to classic ner/ner stacking, but it does not consider the coarse-grained nature of attributes (entities belong to different attribute types) and does not easily take into account the open-world nature of the task. users can search for attribute values that are not yet in the knowledge base or not associated with any known product, making it difficult to predict normalized attribute values directly. ## data in order to have a controlled ground for experimentation, we need (i) a dataset of user queries, and (ii) a knowledge graph containing most entities involved in the user queries. knowledge graphs involving products and relative information are usually called product graphs. ## approach the overall architecture of aven contains three different sub-modules, each responsible for a different task: (i) a mention detection module; (ii) an attribute classification module; (iii) an entity disambiguation module. the three modules are learnt jointly as shown in fig. 2 and each of them contributes to the final loss. the latter is obtained as a weighted sum of the three losses. while the coefficients are currently set to 1 for all the three tasks, we aim to eventually use gradnorm @xcite to tune the loss weights. more formally, let us define @xmath0, . . . , q n as an input query with n tokens/words. we denote by s [i,j] the sub-span q i q i+1 . . . q j . we are interested in three different quantities: m ij refers to span s [i,j] being a mention, a a ij refers to the same span being an attribute value for attribute a, and finally e e ij refers to s [i,j] being an instance of entity e. in the next sections, we will review the three different components. ## experimental results in this section, we present experimental results on two datasets described in section 3.2 and section 3.3. we provide a brief overview of the protocol used in both cases. ## conclusions and future directions in this paper, we introduced a novel approach to tackle qau in a multi-task fashion. we demonstrated its effectiveness on two datasets, compared to some simple baselines. however, further ablation studies on more datasets / baselines (e.g. ayoola et al. 2022) are necessary to assess its generalization power. additionally, future work will focus on improving the multitasking efficiency of aven, for instance by implementing @xcite .