Unsupervised Named Entity Disambiguation for Low Resource Domains (2024)

Abstract: In the ever-evolving landscape of natural language processing and information retrieval, the need for robust and domain-specific entity linking algorithms has become increasingly apparent. It is crucial in a considerable number of fields such as humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge. The use of Named Entity Disambiguation (NED) in such domains requires handling noisy texts, low resource settings and domain-specific KBs. Existing approaches are mostly inappropriate for such scenarios, as they either depend on training data or are not flexible enough to work with domain-specific KBs. Thus in this work, we present a unsupervised approach leveraging the concept of Group Steiner Trees (GST), which can identify the most relevant candidate for entity disambiguation using the contextual similarities across candidate entities for all the mentions present in a document. We outperform the state-of-the-art unsupervised methods by more than 40%(in avg) in terms of Precision@1 and Hit@5 across various domain-specific datasets.

## introduction named entity disambiguation (ned) is the task of resolving the ambiguity associated with entity mentions in a document by linking them to the appropriate entries in a knowledge base (kb). recently, ned has been applied in various fields, including digital humanities, art, architecture, literature, and biomedical science, for tasks such as searching @xcite ), question answering @xcite and information extraction @xcite . the key challenges in such domain specific ned tasks are twofold -(a) they provide little or no training data with ground truth annotations and (b) the associated knowledge graphs (kg) are typically small and with no or very limited entity descriptions @xcite . in order to deal with such challenges, in this work we consider the setting where entity disambiguation is needed to be performed with absolute absence of annotated data. in such constrained scenarios, leveraging the state-of-the-art neural entity linkers become infeasible as they are primarily dependent on a large corpus of annotated data and long enough entity descriptions from kg @xcite . similarly this setting also disqualifies unsupervised ned approaches such as @xcite which rely on labeled data to generate candidate entities such as domainadaptive transformer-based models @xcite , blink (ledell wu, 2020), zeshel @xcite , and auto-regressive models like genre @xcite . in the literature, only a few approaches fit our constrained setting such as graph-based using mention distances @xcite , pagerank/random walk based @xcite , and graph ranking based @xcite . a recent approach by @xcite also explores singular value decomposition, showing gold entities in a low-rank subspace. however, these methods often struggle in achieving the required efficacy while disambiguating entities. in this work, we present a novel unsupervised ned approach for domain specific low-resource scenarios, which leverages the concept of group steiner trees (gsts) @xcite . in this approach, we map the candidate entities for each mention in the document, to nodes in the associated knowledge graph, obtain the subgraph connecting these nodes and then extract minimum cost gsts from this sub-graph. such gsts facilitate collective entity disambiguation exploiting the fact that the entities that are truly mentioned in a document (the 'gold entities') tend to form a dense subgraph among the set of all candidate entities in the document. figure 1 : proposed gst-ned approach: the sample document at the top contains three mentions; the subgraph extracted from the kb is shown at the left and the minimum cost gst is shown using the box at the right. in the induced subgraph, the candidates for 'adenoma' are marked in red, 'hamartomas' are marked in yellow and 'liver rupture' are marked in blue. in summary, our main contributions are the following -(a) we propose an unsupervised group steiner tree based named entity disambiguation (gst-ned) method which is capable to perform ned for low resource domains at the absence of any annotated data; (b) we compare our proposed approach with several state-of-the-art baselines across multiple domain specific datasets and demonstrate its superior performance with significant improvements in the metrics (more than 40% in avg. in precision@1 scores) 1 . ## problem statement similar to most previous works in the ned literature (with a few exceptions @xcite )), we assume that documentwise mention spans (usually obtained by a named entity recognizer) are already provided. let d be a single document from a collection d of documents. also, let m @xmath0, m 2 , . . . , m m } be the set of m mentions contained in d, and let e be the collection of all the entities contained in the reference domain specific knowledge graph kg. the task here is to find, for each mention m i the correct entity @xmath1. typically, given the set of mentions, an ned approach performs the disambiguation in two steps -(a) candidate generation, where candidate entities from the kg are retrieved for each of the mentions, 1 code is available at https://github.com/ deba-iitbh/gst-ned and (b) candidate ranking, where the candidate entities are ranked based on their propensities to be mapped with the corresponding mentions. our primary focus in this study is the candidate ranking/disambiguation step. in the following, we describe our proposed candidate ranking method and mention the approaches adhered for the other step. ## methodology candidate generation we index the domain specific kg and use fuzzy text search @xcite to retrieve candidates based on the surface form of the annotated mention. this is found to be the standard practice in most of the recent unsupervised ned approaches @xcite ) fuzzy text search returns a confidence value with each potential match; we keep only the candidates which are returned with more than 0.75 confidence value (chosen empirically) foot_0 . ## results and discussion we compared our proposed gst-ned approach with other baselines algorithms and the corresponding results are depicted in table . 2. we can observe that our method outperforms the state-of-the-art in all the datasets (especially in terms of p @1). in 1641, the relatively poor performance of all the algorithms stems from the poor recall of the candidate entities (see ## conclusion in this paper, we have addressed the problem of ned of domain-specific corpora in the absence of annotated data. it works based on the intuition that a gold entity candidate from a document should be more cohesively connected with other gold candidates in the knowledge graph compared to other non-gold candidates. we have leveraged the concept of group steiner trees (gsts), that relies solely on the availability of candidate entity names and a domain specific knowledge graph. extraction of minimum cost gsts in our proposed approach gst-ned, ensures that the chosen entities are closely connected in the domain specific knowledge graphs. experiments on benchmark datasets from varied domains have portrayed the effectiveness of our proposed approach against the state-ofthe art unsupervised and zero-shot approaches.