Knowledge Graphs (KGs) are directed labeled graphs representing entities and the relationships between them.Most prior work focuses on supervised or semi-supervised approaches which require large amounts of annotated data.While unsupervised approaches do not need labeled training data, most existing methods either generate too many redundant relations or require manual mapping of the extracted relations to a known schema.To address these limitations, we propose an unsupervised method for KG generation that requires neither labeled data nor manual mapping to the predefined relation schema.Instead, our method leverages sentence-level semantic similarity for automatically generating relations between pairs of entities.Our proposed method outperforms two baseline systems when evaluated over four datasets.
Text segmentation and extraction from unstructured documents can provide business researchers with a wealth of new information on firms and their behaviors.However, the most valuable text is often difficult to extract consistently due to substantial variations in how content can appear from document to document.Thus, the most successful way to extract this content has been through costly crowdsourcing and training of manual workers.We propose the Assisted Neural Text Segmentation (ANTS) framework to identify pertinent text in unstructured documents from a small set of labeled examples.ANTS leverages deep learning and transfer learning architectures to empower researchers to identify relevant text with minimal manual coding.Using a real world sample of accounting documents, we identify targeted sections 96% of the time using only 5 training examples.
This paper presents our study in exploring the task of named entity recognition (NER) in a low resource setting, focusing on few-shot learning on the Sumerian NER task.The Sumerian language is deemed as an extremely low-resource language due to that (1) it is a long dead language, (2) highly skilled language experts are extremely scarce.NER on Sumerian text is important in that it helps identify the actors and entities active in a given period of time from the collections of tens of thousands of texts in building socio-economic networks of the archives of interest.As a text classification task, NER tends to become challenging when the amount of annotated data is limited or the model is required to handle new classes.The Sumerian NER is no exception.In this work, we propose to use two few-shot learning systems, ProtoBERT and NNShot, to the Sumerian NER task.Our experiments show that the Pro-toBERT NER generally outperforms both the NNShot NER and the fully supervised BERT NER in low resource settings on the predictions of rare classes.In particular, F1-score of Pro-toBERT on unseen entity types on our test set has achieved 89.6% that is significantly better than the F1-score of 84.3% of the BERT NER.
The abundance of online information narrows our collective attention span. We address the need for automated claim validation, based on the incorporated evidence from dynamic, textually represented environments created by online news sources. We present FarFetched, an entity-centric reasoning framework based on news, where latent connections between events, actions or statements are discovered via their identified entity mentions and are represented in a structured form with the help of a graph database. We propose an evidence construction approach that combines relevant extracts of the stored information from various online sources in order to support or refute a given claim in free text, relying on entity linking and semantic similarity. We then leverage textual entailment recognition to provide a measurable way for assessing whether this claim is plausible based on the constructed evidence. Our approach tries to fill the gap in automated claim validation for less-resourced languages and is showcased on the Greek language, complemented by the training of relevant semantic textual similarity (STS) and natural language inference (NLI) models that are evaluated on translated versions of common benchmarks.
Supervised event extraction models require a substantial amount of training data to perform well.However, event annotation requires a lot of human effort and costs much time, which limits the application of existing supervised approaches to new event types.In order to reduce manual labor and shorten the time to build an event extraction system for an arbitrary event ontology, we present a new framework to train such systems much more efficiently without large annotations.Our event trigger labeling model uses a weak supervision approach, which only requires a set of keywords, a small number of examples and an unlabeled corpus, on which our approach automatically collects weakly supervised annotations.Our argument role labeling component performs zero-shot learning, which only requires the names of the argument roles of new event types.The source codes of our event trigger detection 1 and event argument extraction 2 models are publicly available for research purposes.We also release a dockerized system connecting the two models into an unified event extraction pipeline 3 .
