On the Relation Type of Fiber Cone

2015 ◽  
Vol 40 (3) ◽  
pp. 535-544
Author(s):  
A. V. Jayanthan ◽  
Ramakrishna Nanduri
Keyword(s):  
Relation Type ◽  
Fiber Cone

Learning Pattern Relation-Based Hyperbolic Embedding for Adverse Drug Reaction Extraction

2021 ◽  
Vol 12 (2) ◽  
pp. 69-87
Author(s):  
Siriwon Taewijit ◽  
Thanaruk Theeramunkong
Keyword(s):  
Hierarchical Structure ◽  
Medical Information ◽  
Hospital Admissions ◽  
Syntactic Structure ◽  
Semantic Representation ◽  
Cartesian Product ◽  
Natural Form ◽  
Relation Type ◽  
Vector Sum ◽  
Hyperbolic Embedding

Hyperbolic embedding has been recently developed to allow us to embed words in a Cartesian product of hyperbolic spaces, and its efficiency has been proved in several works of literature since the hierarchical structure is the natural form of texts. Such a hierarchical structure exhibits not only the syntactic structure but also semantic representation. This paper presents an approach to learn meaningful patterns by hyperbolic embedding and then extract adverse drug reactions from electronic medical records. In the experiments, the public source of data from MIMIC-III (Medical Information Mart for Intensive Care III) with over 58,000 observed hospital admissions of the brief hospital course section is used, and the result shows that the approach can construct a set of efficient word embeddings and also retrieve texts of the same relation type with the input. With the Poincaré embeddings model and its vector sum (PC-S), the authors obtain up to 82.3% in the precision at ten, 85.7% in the mean average precision, and 93.6% in the normalized discounted cumulative gain.

scholarly journals Implicit-Relation-Type Cyclic Contractive Mappings and Applications to Integral Equations

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Hemant Kumar Nashine ◽  
Zoran Kadelburg ◽  
Poom Kumam
Keyword(s):  
Integral Equations ◽  
Existence And Uniqueness ◽  
Contractive Condition ◽  
Uniqueness Of Solutions ◽  
Nonlinear Integral Equations ◽  
Implicit Relation ◽  
Existence And Uniqueness Results ◽  
Contractive Mappings ◽  
Uniqueness Results ◽  
Relation Type

We introduce an implicit-relation-type cyclic contractive condition for a map in a metric space and derive existence and uniqueness results of fixed points for such mappings. Examples are given to support the usability of our results. At the end of the paper, an application to the study of existence and uniqueness of solutions for a class of nonlinear integral equations is presented.

scholarly journals Discourse Structure in Machine Translation Evaluation

2017 ◽  
Vol 43 (4) ◽  
pp. 683-722 ◽  
Author(s):  
Shafiq Joty ◽  
Francisco Guzmán ◽  
Lluís Màrquez ◽  
Preslav Nakov
Keyword(s):  
Machine Translation ◽  
Similarity Measures ◽  
Discourse Structure ◽  
System Level ◽  
Structure Theory ◽  
Evaluation Metrics ◽  
Machine Translation Evaluation ◽  
Sentence Level ◽  
Relation Type ◽  
Parse Trees

In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment level and at the system level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTK party. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular, we show that (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference RST tree is positively correlated with translation quality.

Subtopic annotation and automatic segmentation for news texts in Brazilian Portuguese

Corpora ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. 23-54 ◽  
Author(s):  
Paula C.F. Cardoso ◽  
Thiago A.S. Pardo ◽  
Maite Taboada
Keyword(s):  
Language Processing ◽  
Automatic Segmentation ◽  
Brazilian Portuguese ◽  
Structure Theory ◽  
Corpus Annotation ◽  
Main Research ◽  
Annotation Process ◽  
Relation Type ◽  
Research Questions ◽  
Automatic Summarisation

Subtopic segmentation aims to break documents into subtopical text passages, which develop a main topic in a text. Being capable of automatically detecting subtopics is very useful for several Natural Language Processing applications. For instance, in automatic summarisation, having the subtopics at hand enables the production of summaries with good subtopic coverage. Given the usefulness of subtopic segmentation, it is common to assemble a reference-annotated corpus that supports the study of the envisioned phenomena and the development and evaluation of systems. In this paper, we describe the subtopic annotation process in a corpus of news texts written in Brazilian Portuguese, following a systematic annotation process and answering the main research questions when performing corpus annotation. Based on this corpus, we propose novel methods for subtopic segmentation following patterns of discourse organisation, specifically using Rhetorical Structure Theory. We show that discourse structures mirror the subtopic changes in news texts. An important outcome of this work is the freely available annotated corpus, which, to the best of our knowledge, is the only one for Portuguese. We demonstrate that some discourse knowledge may significantly help to find boundaries automatically in a text. In particular, the relation type and the level of the tree structure are important features.

LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-28
Author(s):  
Surong Yan ◽  
Kwei-Jay Lin ◽  
Xiaolin Zheng ◽  
Haosen Wang
Keyword(s):  
Side Information ◽  
Dimensional Space ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Learning Framework ◽  
Relation Type ◽  
End To End ◽  
Low Dimensional ◽  
Item Knowledge ◽  
Explicit And Implicit Knowledge

Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users’ preferences and items’ features, respectively. Finally, we add virtual “recommendation” relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.

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