1,652 research outputs found
Relation Classification with Limited Supervision
Large reams of unstructured data, for instance in form textual document collections containing entities and relations, exist in many domains. The process of deriving valuable domain insights and intelligence from such documents collections usually involves the extraction of information such as the relations between the entities in such collections. Relation classification is the task of detecting relations between entities. Supervised machine learning models, which have become the tool of choice for relation classification, require substantial quantities of annotated data for each relation in order to perform optimally. For many domains, such quantities of annotated data for relations may not be readily available, and manually curating such annotations may not be practical due to time and cost constraints.
In this work, we develop both model-specific and model-agnostic approaches for relation classification with limited supervision. We start by proposing an approach for learning embeddings for contextual surface patterns, which are the set of surface patterns associated with entity pairs across a text corpus, to provide additional supervision signals for relation classification with limited supervision. We find that this approach improves classification performance on relations with limited supervision instances. However, this initial approach assumes the availability of at least one annotated instance per relation during training. In order to address this limitation, we propose an approach which formulates the task of relation classification as that of textual entailment. This reformulation allows us to use the textual descriptions of relations to classify their instances. It also allows us to utilize existing textual entailment datasets and models to classify relations with zero supervision instances.
The two methods proposed previously rely on the use of specific model architectures for relation classification. Since a wide variety of models have been proposed for relation classification in the literature, a more general approach is thus desirable. We subsequently propose our first model-agnostic meta-learning algorithm for relation classification with limited supervision. This algorithm is applicable to any gradient-optimized relation classification model. We show that the proposed approach improves the predictive performance of two existing relation classification models when supervision for relations is limited. Next, because all the approaches we have proposed so far assume the availability of all supervision needed for classifying relations prior to model training, they are unable to handle the case when new supervision for relations becomes available after training. Such new supervision may need to be incorporated into the model to enable it classify new relations or to improve its performance on existing relations. Our last approach addresses this short-coming. We propose a model-agnostic algorithm which enables relation classification models to learn continually from new supervision as it becomes available, while doing so in a data-efficient manner and without forgetting knowledge of previous relations
Schema-adaptable Knowledge Graph Construction
Conventional Knowledge Graph Construction (KGC) approaches typically follow
the static information extraction paradigm with a closed set of pre-defined
schema. As a result, such approaches fall short when applied to dynamic
scenarios or domains, whereas a new type of knowledge emerges. This
necessitates a system that can handle evolving schema automatically to extract
information for KGC. To address this need, we propose a new task called
schema-adaptable KGC, which aims to continually extract entity, relation, and
event based on a dynamically changing schema graph without re-training. We
first split and convert existing datasets based on three principles to build a
benchmark, i.e., horizontal schema expansion, vertical schema expansion, and
hybrid schema expansion; then investigate the schema-adaptable performance of
several well-known approaches such as Text2Event, TANL, UIE and GPT-3.5. We
further propose a simple yet effective baseline dubbed \textsc{AdaKGC}, which
contains schema-enriched prefix instructor and schema-conditioned dynamic
decoding to better handle evolving schema. Comprehensive experimental results
illustrate that AdaKGC can outperform baselines but still have room for
improvement. We hope the proposed work can deliver benefits to the community.
Code and datasets available at https://github.com/zjunlp/AdaKGC.Comment: EMNLP 2023 (Findings
Improving Continual Relation Extraction through Prototypical Contrastive Learning
Continual relation extraction (CRE) aims to extract relations towards the
continuous and iterative arrival of new data, of which the major challenge is
the catastrophic forgetting of old tasks. In order to alleviate this critical
problem for enhanced CRE performance, we propose a novel Continual Relation
Extraction framework with Contrastive Learning, namely CRECL, which is built
with a classification network and a prototypical contrastive network to achieve
the incremental-class learning of CRE. Specifically, in the contrastive network
a given instance is contrasted with the prototype of each candidate relations
stored in the memory module. Such contrastive learning scheme ensures the data
distributions of all tasks more distinguishable, so as to alleviate the
catastrophic forgetting further. Our experiment results not only demonstrate
our CRECL's advantage over the state-of-the-art baselines on two public
datasets, but also verify the effectiveness of CRECL's contrastive learning on
improving CRE performance
Biomedical ontology alignment: An approach based on representation learning
While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection
Lifelong event detection aims to incrementally update a model with new event
types and data while retaining the capability on previously learned old types.
One critical challenge is that the model would catastrophically forget old
types when continually trained on new data. In this paper, we introduce
Episodic Memory Prompts (EMP) to explicitly preserve the learned task-specific
knowledge. Our method adopts continuous prompt for each task and they are
optimized to instruct the model prediction and learn event-specific
representation. The EMPs learned in previous tasks are carried along with the
model in subsequent tasks, and can serve as a memory module that keeps the old
knowledge and transferring to new tasks. Experiment results demonstrate the
effectiveness of our method. Furthermore, we also conduct a comprehensive
analysis of the new and old event types in lifelong learning.Comment: Accepted to COLING'22 Main Conference (Short paper). 9 pages, 2
figures, 3 table
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