Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEdge: Enriching Knowledge Graph Embeddings with External Text
الحافة: إثراء Anderments Graph Admings مع النص الخارجي
197
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تعاني رسوم الرسوم البيانية المعرفة من Sparsity والتي تتحلل من جودة التمثيلات الناتجة عن الطرق المختلفة. في حين أن هناك وفرة من المعلومات النصية في جميع أنحاء الويب والعديد من قواعد المعرفة الموجودة، فإن محاذاة المعلومات في جميع مصادر البيانات المتنوعة تظل تحديا في الأدبيات. وقد تناولت العمل السابق جزئيا هذه المشكلة عن طريق إثراء كيانات الرسم البياني المعرفي بناء على "حدوث كلمات" بجدية موجودة في كيانات الرسوم البيانية والنص الخارجي، بينما نحقق تكبير "" لينة "من خلال اقتراح إثراء الرسم البياني المعرفي وإطار التضمين اسمه الحافة. بالنظر إلى الرسم البياني المعرفي الأصلي، فإننا نقوم أولا بإنشاء رسم بياني معدني غني ولكن صاخبة يستخدم النصوص الخارجية في المستوى الدلالي والهيكل الهيكلية. لتقطير المعرفة ذات الصلة وقمع الضوضاء المقدمة، نقوم بتصميم مصطلح محاذاة رسم بياني في مساحة تضمين مشتركة بين الرسم البياني الأصلي والرسم البياني المعزز. لتعزيز التعلم التضمين في الرسم البياني المعزز، فإننا نتاجر مواصلة علاقة الموقع بالكيان المستهدف بناء على أخذ العينات السلبية. النتائج التجريبية على أربعة مجموعات بيانات قياسية تثبت متانة وفعالية الحافة في تبديد الارتباط وتصنيف العقدة.
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on hard'' co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve soft'' augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.
References used
https://aclanthology.org/
rate research
Read More
In the paper, we present the process of adding morphological information to the Polish WordNet (plWordNet). We describe the reasons for this connection and the intuitions behind it. We also draw attention to the specificity of the Polish morphology.
We show in which tasks the morphological information is important and how the methods can be developed by extending them to include combined morphological information based on WordNet.
Introducing factors, that is to say, word features such as linguistic information referring to the source tokens, is known to improve the results of neural machine translation systems in certain settings, typically in recurrent architectures. This st
udy proposes enhancing the current state-of-the-art neural machine translation architecture, the Transformer, so that it allows to introduce external knowledge. In particular, our proposed modification, the Factored Transformer, uses linguistic factors that insert additional knowledge into the machine translation system. Apart from using different kinds of features, we study the effect of different architectural configurations. Specifically, we analyze the performance of combining words and features at the embedding level or at the encoder level, and we experiment with two different combination strategies. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both extremely low-resourced and very distant languages, and obtain an improvement of 1.2 BLEU
This study introduces an enriched version of the E2E dataset, one of the most popular language resources for data-to-text NLG. We extract intermediate representations for popular pipeline tasks such as discourse ordering, text structuring, lexicaliza
tion and referring expression generation, enabling researchers to rapidly develop and evaluate their data-to-text pipeline systems. The intermediate representations are extracted by aligning non-linguistic and text representations through a process called delexicalization, which consists in replacing input referring expressions to entities/attributes with placeholders. The enriched dataset is publicly available.
The style transfer task (here style is used in a broad authorial'' sense with many aspects including register, sentence structure, and vocabulary choice) takes text input and rewrites it in a specified target style preserving the meaning, but alterin
g the style of the source text to match that of the target. Much of the existing research on this task depends on the use of parallel datasets. In this work we employ recent results in unsupervised cross-lingual language modeling (XLM) and machine translation to effect style transfer while treating the input data as unaligned. First, we show that adding content embeddings'' to the XLM which capture human-specified groupings of subject matter can improve performance over the baseline model. Evaluation of style transfer has often relied on metrics designed for machine translation which have received criticism of their suitability for this task. As a second contribution, we propose the use of a suite of classical stylometrics as a useful complement for evaluation. We select a few such measures and include these in the analysis of our results.
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Recent studies have leveraged gr
aph neural networks to capture the inter-sentential relationship (e.g., the discourse graph) within the documents to learn contextual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity and natural connection relationships), nor model intra-sentential relationships (e.g, semantic similarity and syntactic relationship among words). To address these problems, we propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive text summarization. Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate effectiveness of our method.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
