We study the problem of few-shot Fine-grained Entity Typing (FET),
where only a few annotated entity mentions with contexts are given
for each entity type. Recently, prompt-based tuning has demonstrated
superior performance to standard fine-tuning in few-shot
scenarios by formulating the entity type classification task as a
“fill-in-the-blank” problem. This allows effective utilization of the
strong language modeling capability of Pre-trained Language Models
(PLMs). Despite the success of current prompt-based tuning
approaches, two major challenges remain: (1) the verbalizer in
prompts is either manually designed or constructed from external
knowledge bases, without considering the target corpus and
label hierarchy information, and (2) current approaches mainly
utilize the representation power of PLMs, but have not explored
their generation power acquired through extensive general-domain
pre-training. In this work, we propose a novel framework for fewshot
FET consisting of two modules: (1) an entity type label interpretation
module automatically learns to relate type labels to the
vocabulary by jointly leveraging few-shot instances and the label
hierarchy, and (2) a type-based contextualized instance generator
produces new instances based on given instances to enlarge the
training set for better generalization. On three benchmark datasets,
our model outperforms existing methods by significant margins.
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Mining Entity Synonyms with Efficient Neural Set Generation
Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.
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- NSF-PAR ID:
- 10125827
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 33
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 249 to 256
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Entity set expansion (ESE) refers to mining ``siblings'' of some user-provided seed entities from unstructured data. It has drawn increasing attention in the IR and NLP communities for its various applications. To the best of our knowledge, there has not been any work towards a supervised neural model for entity set expansion from unstructured data. We suspect that the main reason is the lack of massive annotated entity sets. In order to solve this problem, we propose and implement a toolkit called {DBpedia-Sets}, which automatically extracts entity sets from any plain text collection and can provide a large number of distant supervision data for neural model training. We propose a two-channel neural re-ranking model {NESE} that jointly learns exact and semantic matching of entity contexts. The former accepts entity-context co-occurrence information and the latter learns a non-linear transformer from generally pre-trained embeddings to ESE-task specific embeddings for entities. Experiments on real datasets of different scales from different domains show that {NESE} outperforms state-of-the-art approaches in terms of precision and MAP, where the improvements are statistically significant and are higher when the given corpus is larger.more » « less
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Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., “apple” could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as “distant” supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework.more » « less
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In this presentation, we will introduce the Platform for Author-Driven Computable Data and Ontology Production for Taxonomists, which consists of three components: a web-based, ontology-aware software application called 'Character Recorder,' which features a spreadsheet as the data entry platform and provides authors with the flexibility of using their preferred terminology in recording characters for a set of specimens (this application also facilitates semantic clarity and consistency across species descriptions); a set of services that produce RDF graph data, collects terms added by authors, detects potential conflicts between terms, dispatches conflicts to the third component and updates the ontology with resolutions; and an Android mobile application, 'Conflict Resolver,' which displays ontological conflicts and accepts solutions proposed by multiple experts. a web-based, ontology-aware software application called 'Character Recorder,' which features a spreadsheet as the data entry platform and provides authors with the flexibility of using their preferred terminology in recording characters for a set of specimens (this application also facilitates semantic clarity and consistency across species descriptions); a set of services that produce RDF graph data, collects terms added by authors, detects potential conflicts between terms, dispatches conflicts to the third component and updates the ontology with resolutions; and an Android mobile application, 'Conflict Resolver,' which displays ontological conflicts and accepts solutions proposed by multiple experts. 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The key difference of this system from other ontology-aware systems is that authors can directly add needed terms to the ontology as they wish and can update their data according to ontology updates. a report on the findings from a recent survey of 90+ participants on the need for a tool like Character Recorder; a methods section that describes how we provide semantics to an existing vocabulary of quantitative characters through a set of properties that explain where and how a measurement (e.g., length of perigynium beak) is taken. We also report on how a custom color palette of RGB values obtained from real specimens or high-quality specimen images, can be used to help authors choose standardized color descriptions for plant specimens; and a software demonstration, where we show how Character Recorder and Conflict Resolver can work together to construct both human-readable descriptions and RDF graphs using morphological data derived from species in the plant genus Carex (sedges). 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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aaai.v33i01.3301249
