LATEX -Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes (2021)

Abstract: In this paper, we present LATEX-Numeric - a high-precision fully-automated scalable framework for extracting E-commerce numeric attributes from unstructured product text like product description. Most of the past work on attribute extraction is not scalable as they rely on manually curated training data, either with or without use of active learning. We rely on distant supervision for training data generation, removing dependency on manual labels. One issue with distant supervision is that it leads to incomplete training annotation due to missing attribute values while matching. We propose a multi-task learning architecture to deal with missing labels in the training data, leading to F1 improvement of 9.2% for numeric attributes over state-of-the-art single-task architecture. While multi-task architecture benefits both numeric and non-numeric attributes, we present automated techniques to further improve the numeric attributes extraction models. Numeric attributes require a list of units (or aliases) for better matching with distant supervision. We propose an automated algorithm for alias creation using unstructured text and attribute values, leading to a 20.2% F1 improvement. Extensive experiments on real world datasets for 20 numeric attributes across 5 product categories and 3 English marketplaces show that LATEX-numeric achieves a high F1-score, without any manual intervention, making it suitable for practical applications. Finally we show that the improvements are language-agnostic and LATEX-Numeric achieves 13.9% F1 improvement for 3 non-English languages.

## latex-numeric framework we pose the attribute extraction problem (refer figure 1 ) as a ner problem, where product attributes are treated as named entities. formally, we are given a text x with a particular tokenization (x 1 , x 2 ,.....x m ) and a set of attributes a: the task is to extract v @xmath0, m] where @xmath1, n] and α 0 represents 'other' entity. figure 2 gives an overview of our proposed latex-numeric framework. we are given a list of pre-defined numeric attributes, dump of prod-ucts consisting of product text and existing attribute values. the attribute values are decimals (e.g. 16) and have an underlying unit (e.g. gb). we term this underlying unit as the canonical unit. for creating distant supervision-based training annotations, we use these canonical units and combine them with attribute values for matching with product text. this serves as the 'canonical aliasing' baseline for our comparisons. we use bio tagging scheme for our experiments as it is a popular format. for training, we use the recently proposed bilstm-cnn-crf model @xcite . this model consists of cnn architecture to encode character information, lstm-based encoder to model contextual information of each token and a crf based tag decoder, which exploits the labels of neighboring tokens for improved classification. unlike open-tag @xcite , we don't use attention as we didn't observe any improvements with use of attention in our initial experiments. creating training annotations using distant supervision may lead to partial annotations due to missing attribute values. in section 3.1, we describe our proposed multi-task learning architecture to deal with such partial annotations. additionally, we have observed that sellers use multiple surface forms to mention attributes in product text (e.g. '3mp', '3mpix', '3 megapixels' for resolution) and hence, distant supervision with just canonical units (e.g. 'mp') may lead to suboptimal training annotations. curating a list of these diverse surface forms will help improve the quality of training annotations. we describe an automated approach for curating such diverse units and improving training annotations of numeric attributes in section 3.2. ## experimental setup and results we picked five product categories and their 20 numeric attributes for three english marketplaces (in, us and uk). we extracted product data (product description and attribute values) for these categories and split this data into two parts (80% train and 20% test). the train part is used for automated alias creation and creation of training annotations with distant supervision. from the test part, we randomly picked products for each category and label the mention of category-specific numeric attributes in text. out of the total labelled attribute-product pairs, we observed mention of 6.9k attributes in product text. we term training data for english as 'train-en' and audited test dataset as 'test-en' (details in ## conclusion in this paper, we described 'latex-numeric', a high-precision fully-automated framework for training attribute extraction models for ecommerce numeric attributes. we characterized the problem of missing-pa that arises with distant supervision due to missing attribute values and proposed a multi-task learning architecture to alleviate the missing-pa problem, leading to 9.2% f1 improvement for numeric attributes. we established the applicability of our proposed multi-task architecture for textual attributes and bert as underlying model as well. additionally, we proposed an automated algorithm for alias creation, to deal with variations of numeric attribute mentions, leading to models with 20.2% f1 improvement. our evaluation on three romance languages establishes that these improvements are applicable across non-english languages as well. models trained using our proposed latex-numeric framework achieve high f1 score, making them suitable for practical applications.