Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach (2023)

Abstract: We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention. Our supervision comes from a high-quality seed attribute set bootstrapped from existing resources, and we aim to expand the attribute vocabulary of existing seed types, and also to discover any new attribute types automatically. A new dataset is created to support our setting, and our approach Amacer is proposed specifically to tackle the limited supervision. Especially, given that no direct supervision is available for those unseen new attributes, our novel formulation exploits self-supervised heuristic and unsupervised latent attributes, which attains implicit semantic signals as additional supervision by leveraging product context. Experiments suggest that our approach surpasses various baselines by 12 F1, expanding attributes of existing types significantly by up to 12 times, and discovering values from 39% new types.

## introduction attribute mining (or product attribute extraction) is to extract values of various attribute types (e.g. colors, flavors) from e-commerce product description, which is a foundational piece for product understanding in online shopping services, enabling better search and recommendation experience. within this task regime, different settings have been studied. most pioneer works deem it as a closed-world setting, where models are trained to identify a fixed set of pre-defined attribute types @xcite @xcite , similar to the standard named entity recognition (ner). recent works start to step up towards the open-world aspect that supports titles: black tea with orange & sweet clove -2.67 ounces, 30 sachets bullet point description: ## data our dataset woam consists of three parts, including: 1) text corpus; 2) seed attribute set for training; 3) human-annotated test set for evaluation. full statistics of our dataset are provided in corpus four common e-commerce product categories are included in our corpus: tea, vitamin, sofa, phone case. for each category, we sampled 9,000+ products publicly listed on amazon.com with full description available in english. each product record can be represented as a tuple: (identifier, category, title, bullet points). seed set for each category, the seed set consists of a few applicable attribute types (avg. 16.5 types per category) and their values (avg. 22 values per type). we adopt a hybrid approach for the construction: existing resources are first utilized to bootstrap the seed set, and human curation is performed upon to overcome the noisy issue existed in previous datasets (example shown in human curation: as the attribute set after sanitizing is relatively small, human curators can go through the entire set rather quickly and consolidate the final seed set (< 40 min per product category). concretely, remaining noisy values are spotted and removed from their attribute types. furthermore, granularity is adjusted such that ambiguous or coarse attribute types are split into multiple newly defined fine-grained types; similar attribute types are also merged into one type. after we obtain the final seed set, we perform string match to obtain their occurrences in corpus, ready to be used for training. a development set is separately created that consists of sanitized profile attributes solely for hyperparameter tuning. overall, our training supervision is built practically that balances between scalability and quality. test set for each category, we collect additional products not covered in the raw corpus as the test set. two in-house annotators are asked to annotate all spans that appear as reasonable attribute values of either an existing type from the seed set, or a brand-new type that fits the context. as with previous works, we do not allow overlapping spans: more complete spans are preferred over shorter and incomplete spans; each span is assigned a single attribute type that best describes its property. our proposed approach for this dataset is presented in the following section 3-6. specifically, section 3-5 introduce the overall pipeline depicted in figure 2 that utilizes explicit signals from seed attributes, and section 6 introduces our novel for-mulation to exploit implicit signals beyond the limited seed attributes. ## candidate span generation the first stage of our approach is to generate spans from product description that could be qualified as attribute values, producing a set of non-overlapping candidate spans, serving as a foundational step for this attribute extraction task. with weak supervision in mind, this step should not simply rely on signals from the seed set; otherwise, it would become hard to generalize and lose diverse attribute expressions during inference. therefore, directly employing a supervised model can be suboptimal. it is also tempting to use off-theshelf phrase extraction tools such as autophrase @xcite , however, the domain shift on e-commerce description of varied categories can severely affect recall, as observed by @xcite . the close work oa-mine regards this stage as an unsupervised sentence segmentation task on product titles through language model probing @xcite , regarding each segment as a candidate span. nonetheless, two shortcomings still remain. first, unlike titles, segmentation may not be suitable for bullet points, as most segments from bullet points would be noisy spans, demonstrated by the lower value density (13.8%) in in this work, we instead resort to a basic yet effective strategy that overcomes above issues, by using syntax-oriented patterns: we collect valid part-of-speech (pos) patterns for attribute values, and simply obtain all spans in the corpus that fit into those patterns as candidate spans, followed by rudimentary stopword filtering and overlapping span removal (prioritizing longer spans), yielding a smaller but higher-quality candidate set than that from sentence segmentation. valid pos patterns are acquired in a data-driven fashion without human intervention: we leverage the product profiles again, and obtain all pos sequences of their attribute values. these raw sequences are further compacted by removing consecutive duplicate pos tags, such that healthy clean water ([adj, adj, noun] → [adj, noun]) will share the same pos pattern as clean water ([adj, noun]). the resulting set of collected pos patterns serves to identify spans as well-formed or ill-formed phrases. examples of our pos patterns are shown in as we depend on external tools to identify pos, this process is not without noises. nonetheless, we find the empirical performance to be quite robust qualitatively. moreover, it can be augmented with other techniques to mitigate noise in scenarios tailored to specific applications. ## explicit signals for seed expansion with both seed attribute values and candidate spans in-place, our next objective is to perform representation learning that refines the geometry of embedding space, such that values of similar attributes should have a closer embedding representation, and vice versa, as the key property to leverage in later grouping stage. in this section, we introduce the utilization of available seed attributes as explicit supervision, primarily targeting the vocabulary expansion of existing attribute types. for each seed value or candidate span, we can have an initial representation on the embedding space via encoding through pretrained language models such as bert @xcite . concretely, we feed each text sequence (either a title or bullet point) to bert, and obtain the contextualized representation of each span by averaging its token embedding, without introducing extra encoding parameters. supervised contrastive learning contrastive learning is a natural fit to consume task signals from the seed set: for an anchor seed value v a , a positive seed v p from the same attribute, and a negative seed v n from a different attribute, contrastive learning enforces (v a , v p ) to be more similar than (v a , v n ) on the embedding space. oa-mine adopts a triplet loss @xcite for the supervised contrastive learning, as well as another regression loss (reimers and gurevych, 2019) that directly pushes the similarity of positive/negative pairs, requiring careful sampling and tuning. in our work, we simplify this supervised process by only using an in-batch negative contrastive loss @xcite . let i s be all seed value indices, p s (i) be the indices of positive seeds that belong to the same attribute as seed i, n s (i) = i s \ p s (i) be the corresponding negative seeds. g i is the l2-normalized embedding of seed i from the last layer of bert encoding. the loss can then be denoted as: ## candidate span grouping after representation learning, a grouping stage upon candidate spans is followed. each resulting cluster represents an attribute type, with each span inside being its attribute value. unlike most related works that employ off-the-shelf clustering algorithms such as hac, k-means or dbscan @xcite @xcite , we propose a more fine-grained grouping strategy, which first explicitly addresses the expansion of existing seed attributes, then discovers new potential attributes, as described below. ## implicit signals for new discovery since the seed set only provides semantic signals regarding seed attributes, the majority of candidate spans lack proper supervision, as most of them are absent from the seed set, especially for those new attributes that have no direct supervision during representation learning. therefore, it is desirable to exploit additional implicit signals towards more new-attribute-friendly embedding space, and we propose novel methods to tackle the challenge by fully leveraging product context through selfsupervised and unsupervised regularization. ## experiments experiments are conducted on our dataset in multiple model settings, including various baselines. three different types of models are examined based on how attribute spans are obtained: for candidate span generation, we use spacy foot_1 to obtain pos tags; a total of 96 valid pos patterns see section 7 for detailed specifications of model settings and evaluation metrics. each number is the macro-average across all product categories. models with lower recall tend to have higher jaccard/ari/nmi scores, as they produce fewer (and easier) attribute clusters of higher purity. the best performance by both exact/partial-f1 is the underlined score achieved by our approach amacer (statistically significant from t-@xmath0. evaluation metrics standard clustering evaluation metrics are used: jaccard, adjusted rand index (ari), normalized mutual information (nmi), to compare the attribute assignments on gold spans; recall, to evaluate gold cluster coverage. as above metrics are consistent with oa-mine, the evaluation adopts exact-match on predicted/gold spans. however, it could become over-restrictive as span boundaries can be quite subjective in this openworld setting, losing the information of near-correct predictions. thus, we also provide a relaxed evaluation that allows partial-match on spans, such that a predicted span is considered an attribute value if more than half of the span falls into a gold value. to assess the overall performance of a model, we roughly regard the averaged number of jaccard, ari and nmi as pseudo precision, and derive a single pseudo-f1 score based on the clustering precision and recall, serving as the main evaluation metric of each approach. ## quantitative analysis to quantify the unique challenges of this task, we decompose the evaluation to examine two perspectives specifically: • performance on new attribute types (only openworld evaluation) compared to seed types (only closed-world evaluation). for more traits of our corpus, all models struggle to keep up the performance on bullet points compared to titles, showing that they are indeed harder to extract from due to their characteristics ( to detach the impact of candidate generation, we provide additional views to assess the representation learning and grouping performance. the last column of new attributes: it is inevitably difficult to discover values of new types, as models possess little prior knowledge as regards. for error analysis, we found that for most of these new types, their values are either absent in the predictions, or grouped as other existing attributes mistakenly. wormwood ( artemisia absinthium ) is a bitter herb found in eurasia , north africa , and north america . an example of the latter case; however, it is still encouraging that these new values are extracted and recognized as certain attributes, rather than being neglected by the model, which partially achieves the open-world discovery objective. latent attributes: ## conclusion in this work, we present a new task setting as a practical solution to mine open-world attributes without extensive human intervention. a new dataset is created accordingly, and our proposed approach is designed for light supervision, especially by utilizing a high-quality seed set, as well as exploiting self-supervised and unsupervised semantic signals from the context. empirical results show that our approach effectively improves discovery upon baselines on both existing and new attribute types.