A t TG en: Attribute Tree Generation for Real-World Attribute Joint Extraction (2023)

Abstract: Attribute extraction aims to identify attribute names and the corresponding values from descriptive texts, which is the foundation for extensive downstream applications such as knowledge graph construction, search engines, and e-Commerce. In previous studies, attribute extraction is generally treated as a classification problem for predicting attribute types or a sequence tagging problem for labeling attribute values, where two paradigms, i.e., closed-world and open-world assumption, are involved. However, both of these paradigms have limitations in terms of real-world applications. And prior studies attempting to integrate these paradigms through ensemble, pipeline, and co-training models, still face challenges like cascading errors, high computational overhead, and difficulty in training. To address these existing problems, this paper presents Attribute Tree, a unified formulation for real-world attribute extraction application, where closed-world, open-world, and semi-open attribute extraction tasks are modeled uniformly. Then a text-to-tree generation model, AtTGen, is proposed to learn annotations from different scenarios efficiently and consistently. Experiments demonstrate that our proposed paradigm well covers various scenarios for real-world applications, and the model achieves state-of-the-art, outperforming existing methods by a large margin on three datasets. Our code, pretrained model, and datasets are available at https://github.com/lsvih/AtTGen .

## introduction attribute extraction (ae) is a practical application of the information extraction (ie) task, aiming to identify the attribute name and the corresponding attribute value from unstructured or semistructured text fragments @xcite @xcite . figure 1 shows a typical product profile with extracted attribute tags. as the foundation for various downstream applications such as knowledge graph construction, search engines, e-commerce and recommender systems, ae has attracted extensive research interest in recent years @xcite @xcite @xcite . ## preliminary we first formalize the definition of two mainstream paradigms widely used in attribute extraction. definition 1 (closed-world assumption). cwa ae receives a descriptive text @xmath0, t 2 , ...], e.g. a product title, and a pre-defined schema a which contains a set of attributes (i.e., attribute vocabulary) to extract all attribute @xmath1, @xmath2, where @xmath3, and @xmath4. definition 2 (open-world assumption). owa ae takes a descriptive text @xmath5, t 2 , ...] as input, and the target is to discover all attribute @xmath6, @xmath7, where both the attribute name n and the attribute value v are from the given text, i.e. @xmath8. as stated in section 1, individual one of the above paradigms does not always work well in real-world applications, and the pipeline approach adopted by @xcite to merge the results of the two paradigms would introduce problems such as cascading errors. therefore, we propose a formal definition of real-world ae and its solution in the following sections. ## problem formalization section 1 has expounded that attribute extraction in real-world applications sometimes needs both the guidance of the schema and the ability to extract free attributes from texts. it is actually an extensive aggregation covering both cwa and owa ae, as well as a semi-open scenario where attribute names can be obtained from both. therefore we formally define the real-world attribute extraction as: definition 3 (real-world attribute extraction). given a text t , and an optional a, "real-world ae" is to fill the explicit slots for the optional category in a, or to dig more free attributes from t , or to capture attributes from both a and t . i.e., the final result of real-world ae is a set of attribute @xmath9, @xmath10, @xmath11, ∅} ∪ {t , ∅} and h ̸ = ∅. to implement such an extraction paradigm uniformly, we devise a principled structure, attribute tree, to formally model the target of all real-world ae circumstances: definition 4 (attribute tree). an attribute tree t for a descriptive sentence sent is an unweighted tree with a fixed height @xmath12. all the branches of the tree t have a determined order (r, v, n), and the root r is the only entry node that can be either empty ∅ or the head entity (also called the subject) subj of the attributes. figure 2 visualizes the attribute tree and its instances. the path from the root to the leaves is also the reasoning path of the proposed model. borrowing the notation from epistemology @xcite , there are: definition 5 (subject guidance). setting the subject subj of a descriptive sentence sent as the root node r of the corresponding attribute tree t when available, i.e. let @xmath13, is called enabling the subject guidance. as attributes typically characterize entities and are strongly bound to the subject, we naturally introduce the subject guidance for ae in such a way and the effectiveness has been preliminarily demonstrated in @xcite ; @xcite . ## methodology we design a unified tree generative model attgen, committing to jointly extracting attribute names and values under various scenarios in the real world. it is partially inspired by the success of seq2tree models @xcite @xcite and pointer-copy based spanselector @xcite in other tasks. the overall architecture is shown in figure 3 , and we demonstrate the model details in the following subsections. ## conclusion and future work in this paper, we formulate the real-world ae task into a unified attribute tree, and propose a simple gold standard: <overall length, 79.90 @xmath14, 13.40 @xmath15, 7.50 @xmath16, november 11, 2011> bert: <overall length, 79.90 @xmath17, 13.40 @xmath18, 7.50 @xmath19: <overall length, 79.90 @xmath20, 13.40 @xmath21, 7.50 @xmath22, november 11, 2011> on november 11, 2011, nantong yahwa shipbuilding's newly built bulk carrier "yahwa 104", with an overall length of 79.90 meters, an overall width of 13.40 meters and a depth of 7.50 meters, was successfully sea trialed.