scholarly journals Fine-Grained Entity Typing for Domain Independent Entity Linking

2020 ◽  
Vol 34 (05) ◽  
pp. 8576-8583 ◽  
Author(s):  
Yasumasa Onoe ◽  
Greg Durrett
Keyword(s):  
State Of The Art ◽  
Neural Model ◽  
Test Time ◽  
Entity Linking ◽  
Neural Models ◽  
Label Data ◽  
Fine Grained ◽  
Independent Entity ◽  
Domain Independent ◽  
Better Than

Neural entity linking models are very powerful, but run the risk of overfitting to the domain they are trained in. For this problem, a “domain” is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset. We tackle the problem of building robust entity linking models that generalize effectively and do not rely on labeled entity linking data with a specific entity distribution. Rather than predicting entities directly, our approach models fine-grained entity properties, which can help disambiguate between even closely related entities. We derive a large inventory of types (tens of thousands) from Wikipedia categories, and use hyperlinked mentions in Wikipedia to distantly label data and train an entity typing model. At test time, we classify a mention with this typing model and use soft type predictions to link the mention to the most similar candidate entity. We evaluate our entity linking system on the CoNLL-YAGO dataset (Hoffart et al. 2011) and show that our approach outperforms prior domain-independent entity linking systems. We also test our approach in a harder setting derived from the WikilinksNED dataset (Eshel et al. 2017) where all the mention-entity pairs are unseen during test time. Results indicate that our approach generalizes better than a state-of-the-art neural model on the dataset.

scholarly journals A Goal-Driven Tree-Structured Neural Model for Math Word Problems

2019 ◽  
Author(s):  
Zhipeng Xie ◽  
Shichao Sun
Keyword(s):  
Word Problems ◽  
State Of The Art ◽  
Neural Model ◽  
Neural Models ◽  
Top Down ◽  
Feedforward Networks ◽  
Whole Process ◽  
Expression Tree ◽  
Generate Solution ◽  
Human Problem

Most existing neural models for math word problems exploit Seq2Seq model to generate solution expressions sequentially from left to right, whose results are far from satisfactory due to the lack of goal-driven mechanism commonly seen in human problem solving. This paper proposes a tree-structured neural model to generate expression tree in a goal-driven manner. Given a math word problem, the model first identifies and encodes its goal to achieve, and then the goal gets decomposed into sub-goals combined by an operator in a top-down recursive way. The whole process is repeated until the goal is simple enough to be realized by a known quantity as leaf node. During the process, two-layer gated-feedforward networks are designed to implement each step of goal decomposition, and a recursive neural network is used to encode fulfilled subtrees into subtree embeddings, which provides a better representation of subtrees than the simple goals of subtrees. Experimental results on the dataset Math23K have shown that our tree-structured model outperforms significantly several state-of-the-art models.

Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

2018 ◽  
Vol 14 (3) ◽  
pp. 134-166 ◽  
Author(s):  
Amit Singh ◽  
Aditi Sharan
Keyword(s):  
Fuzzy Logic ◽  
Semantic Web ◽  
Linked Data ◽  
State Of The Art ◽  
Fuzzy Programming ◽  
Data Sources ◽  
Fuzzy Approach ◽  
Entity Linking ◽  
Efficient Information ◽  
Domain Independent

This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.

scholarly journals Multi-Labeled Relation Extraction with Attentive Capsule Network

2019 ◽  
Vol 33 ◽  
pp. 7484-7491 ◽  
Author(s):  
Xinsong Zhang ◽  
Pengshuai Li ◽  
Weijia Jia ◽  
Hai Zhao
Keyword(s):  
Performance Improvement ◽  
Loss Function ◽  
State Of The Art ◽  
Routing Algorithm ◽  
Relation Extraction ◽  
Neural Models ◽  
Novel Approach ◽  
Art Works ◽  
Significant Performance ◽  
Better Than

To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.

scholarly journals A new dataset of dog breed images and a benchmark for finegrained classification

2020 ◽  
Vol 6 (4) ◽  
pp. 477-487
Author(s):  
Ding-Nan Zou ◽  
Song-Hai Zhang ◽  
Tai-Jiang Mu ◽  
Min Zhang
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Whole Body ◽  
Classification Models ◽  
Neural Models ◽  
Fine Grained ◽  
Image Dataset ◽  
Dog Breed ◽  
Bounding Boxes

AbstractIn this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. It is currently the largest dataset for fine-grained classification of dogs, including 130 dog breeds and 70,428 real-world images. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. In comparison to previous similar datasets, it contains more breeds and more carefully chosen images for each breed. The diversity within each breed is greater, with between 200 and 7000+ images for each breed. Annotation of the whole body and head makes the dataset not only suitable for the improvement of finegrained image classification models based on overall features, but also for those locating local informative parts. We show that dataset provides a tough challenge by benchmarking several state-of-the-art deep neural models. The dataset is available for academic purposes at https://cg.cs.tsinghua.edu.cn/ThuDogs/.

scholarly journals Neural Unsupervised Semantic Role Labeling

2021 ◽  
Vol 20 (6) ◽  
pp. 1-16
Author(s):  
Kashif Munir ◽  
Hai Zhao ◽  
Zuchao Li
Keyword(s):  
Argument Structure ◽  
State Of The Art ◽  
Neural Model ◽  
Semantic Structure ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Neural Models ◽  
Previous State ◽  
Dependency Relations ◽  
Predicate Argument Structure

The task of semantic role labeling ( SRL ) is dedicated to finding the predicate-argument structure. Previous works on SRL are mostly supervised and do not consider the difficulty in labeling each example which can be very expensive and time-consuming. In this article, we present the first neural unsupervised model for SRL. To decompose the task as two argument related subtasks, identification and clustering, we propose a pipeline that correspondingly consists of two neural modules. First, we train a neural model on two syntax-aware statistically developed rules. The neural model gets the relevance signal for each token in a sentence, to feed into a BiLSTM, and then an adversarial layer for noise-adding and classifying simultaneously, thus enabling the model to learn the semantic structure of a sentence. Then we propose another neural model for argument role clustering, which is done through clustering the learned argument embeddings biased toward their dependency relations. Experiments on the CoNLL-2009 English dataset demonstrate that our model outperforms the previous state-of-the-art baseline in terms of non-neural models for argument identification and classification.

scholarly journals MEAL: Multi-Model Ensemble via Adversarial Learning

2019 ◽  
Vol 33 ◽  
pp. 4886-4893 ◽  
Author(s):  
Zhiqiang Shen ◽  
Zhankui He ◽  
Xiangyang Xue
Keyword(s):  
Learning Strategy ◽  
Original Model ◽  
Test Time ◽  
Neural Models ◽  
Adversarial Learning ◽  
Base Level ◽  
Trained Teacher ◽  
Test Sets ◽  
Time Required ◽  
Better Than

Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi-original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR-10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top-1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%.

scholarly journals PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Majid Asgari-Bidhendi ◽  
Mehrdad Nasser ◽  
Behrooz Janfada ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
Relation Extraction ◽  
Neural Model ◽  
Semantic Relations ◽  
Base Population ◽  
Neural Models ◽  
Persian Language ◽  
Knowledge Base Population

Relation extraction is the task of extracting semantic relations between entities in a sentence. It is an essential part of some natural language processing tasks such as information extraction, knowledge extraction, question answering, and knowledge base population. The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big data in the Persian language for different applications. In this paper, we present “PERLEX” as the first Persian dataset for relation extraction, which is an expert-translated version of the “SemEval-2010-Task-8” dataset. Moreover, this paper addresses Persian relation extraction utilizing state-of-the-art language-agnostic algorithms. We employ six different models for relation extraction on the proposed bilingual dataset, including a non-neural model (as the baseline), three neural models, and two deep learning models fed by multilingual BERT contextual word representations. The experiments result in the maximum F1-score of 77.66% (provided by BERTEM-MTB method) as the state of the art of relation extraction in the Persian language.

scholarly journals Language and Domain Independent Entity Linking with Quantified Collective Validation

2015 ◽  
Author(s):  
Han Wang ◽  
Jin Guang Zheng ◽  
Xiaogang Ma ◽  
Peter Fox ◽  
Heng Ji
Keyword(s):  
Entity Linking ◽  
Independent Entity ◽  
Domain Independent

scholarly journals Meaningful Answer Generation of E-Commerce Question-Answering

2021 ◽  
Vol 39 (2) ◽  
pp. 1-26
Author(s):  
Shen Gao ◽  
Xiuying Chen ◽  
Zhaochun Ren ◽  
Dongyan Zhao ◽  
Rui Yan
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
State Of The Art ◽  
Neural Model ◽  
Product Reviews ◽  
Neural Models ◽  
Product Attributes ◽  
Human Evaluation ◽  
Final Answer ◽  
Answer Pattern

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the Meaningful Product Answer Generator ( MPAG ), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews . We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

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