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A semantic matching energy function for learning with multi-relational data

Antoine Bordes, Xavier Glorot, Jason Weston, Yoshua Bengio
2013 Machine Learning  
In addition, we present how our method can be applied to perform word-sense disambiguation in a context of open-text semantic parsing, where the goal is to learn to assign a structured meaning representation  ...  Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval,  ...  Acknowledgements The authors would like to acknowledge Léon Bottou, Ronan Collobert, Nicolas Usunier, Nicolas Le Roux, Rodolphe Jenatton and Guillaume Obozinski for inspiring discussions.  ... 
doi:10.1007/s10994-013-5363-6 fatcat:jq72hbr43rcm3ktfgujg2keba4

Introduction to the special issue on learning semantics

Antoine Bordes, Léon Bottou, Ronan Collobert, Dan Roth, Jason Weston, Luke Zettlemoyer
2013 Machine Learning  
A growing number of efforts to develop machine learning approaches for semantic analysis now aim to find (in an automated way) these interpretations (Miller et al.  ...  A key ambition of AI is to render computers able to evolve and interact with the real world.  ...  We wish to sincerely thank all the authors for submitting their work to this special issue.  ... 
doi:10.1007/s10994-013-5381-4 fatcat:7i5ubznmabewldc5asxj3xqfru

A Framework for Employee Appraisals Based on Inductive Logic Programming and Data Mining Methods [chapter]

Darah Aqel, Sunil Vadera
2013 Lecture Notes in Computer Science  
To be specific, the result of disambiguating the sense of each target word in the objectives by the SR-AW algorithm is provided to the WordNet::QueryData module for determining a semantic relation of domain  ...  Korhonen (2002) applies a method for semiautomatic semantic classification of verbs into Levin classes (Levin, 1993) 3 Word Sense Disambiguation This section describes the word sense disambiguation  ...  Appendix A A1 The Grammar Rules Learned by ALEPH from the First Corpus The following presents the grammar rules for SMART objectives learned by ALEPH from the corpus of objectives related to the sales  ... 
doi:10.1007/978-3-642-38824-8_49 fatcat:3bcsstk5tnhobijhl2gmpdi2jy

Semantic Information Retrieval based on Wikipedia Taxonomy

May Sabai Han
2012 International Journal of Computer Applications Technology and Research  
Determining semantic similarity between two terms is a crucial problem in Web Mining for such applications as information retrieval systems and recommender systems.  ...  Information retrieval is used to find a subset of relevant documents against a set of documents.  ...  Also it has proved a significant result in word sense disambiguation.  ... 
doi:10.7753/ijcatr0201.1016 fatcat:tbteoangtfgrphg4pmdvz6rdbq

Knowledge Derived From Wikipedia For Computing Semantic Relatedness

S. P. Ponzetto, M. Strube
2007 The Journal of Artificial Intelligence Research  
Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet.  ...  Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications.  ...  The first author has been supported by a KTF grant (09.003.2004). We thank three anonymous JAIR reviewers for their extensive reviews and our colleague Vivi Nastase for useful feedback.  ... 
doi:10.1613/jair.2308 fatcat:idzadhf375e6zeoivpzzbu3vum

TH_WSD: Thai Word Sense Disambiguation Using Cross-Language Knowledge Sources Approach

J. L. Mitrpanont, P. Chongcharoen
2015 Journal of clean energy technologies  
This paper presents the TH_WSD, a framework for Thai word ambiguous resolution using cross-language knowledge sources of AsianWordNet (AWN) and PrincetonWordNet (PWN) for lexical and word sense explorers  ...  The ambiguity in Thai Word is still a significant issue in translating Thai language to English.  ...  The knowledge-based approach disambiguates word sense by matching context with information from knowledge source [1] which consists of the dictionary, semantic network structure and definitions for the  ... 
doi:10.7763/ijcte.2015.v7.997 fatcat:3lhiazjyvzcuxoazltseb2f3i4

A Hybrid Environment for Syntax-Semantic Tagging [article]

Lluis Padro
1998 arXiv   pre-print
The constraints enable the use of a real value statind "compatibility". The technique is applied to POS tagging, Shallow Parsing and Word Sense Disambigation. Experiments and results are reported.  ...  The thesis describes the application of the relaxation labelling algorithm to NLP disambiguation. Language is modelled through context constraint inspired on Constraint Grammars.  ...  When disambiguating a new occurrence of a word, the chosen sense is that with highest matching ratio between the sense salient words list and the current context. • Another interesting possibility for  ... 
arXiv:cmp-lg/9802002v1 fatcat:yv7cbfrh7ngitorwet2kr6wyxe

Word Sense Disambiguation for Assamese

Jumi Sarmah, Shikhar Kr. Sarma
2016 2016 IEEE 6th International Conference on Advanced Computing (IACC)  
Resolution of lexical ambiguity, commonly known as Word Sense Disambiguation (WSD) task is to distinguish the correct sense among the set of senses for an ambiguous term depending on the particular context  ...  Our future work aims to develop a model for the WSD problem which is fast, optimal and efficient in terms of accuracy and scalability.  ...  In polysemy, a word is associated with more than one meaning which is traditionally called as senses and is distinct but related in some semantic way.  ... 
doi:10.1109/iacc.2016.36 fatcat:5h2jiu5uezcg5ipcrrvdrio634

Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with Language

Sebastian Krause, Leonhard Hennig, Aleksandra Gabryszak, Feiyu Xu, Hans Uszkoreit
2015 Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications  
We present sar-graphs, a knowledge resource that links semantic relations from factual knowledge graphs to the linguistic patterns with which a language can express instances of these relations.  ...  An initial dataset of English sar-graphs for 25 relations is made publicly available, together with a Java-based API.  ...  We would also like to thank Min Fang and Hong Li for their help with the implementation.  ... 
doi:10.18653/v1/w15-4204 dblp:conf/acl-ldl/KrauseHGXU15 fatcat:icxkbg5ibreuflooiui7prgwqi

Automatically acquiring a semantic network of related concepts

Sean Szumlanski, Fernando Gomez
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
Our algorithm then capitalizes on salient sense clustering among these semantic associates to automatically disambiguate them to their corresponding WordNet noun senses (i.e., concepts).  ...  The resultant concept-to-concept associations, stemming from 7,593 target nouns, with 17,104 distinct senses among them, constitute a large-scale semantic network with 208,832 undirected edges between  ...  CHAPTER 5: COARSE-GRAINED WORD SENSE DISAMBIGUATION: AN APPLICATION In the preceding chapters, we presented a method for automatically acquiring a semantic network of related concepts, or noun senses,  ... 
doi:10.1145/1871437.1871445 dblp:conf/cikm/SzumlanskiG10 fatcat:zhykl7wwkrfcjdigpe633odjhi

TASTY: Interactive Entity Linking As-You-Type

Sebastian Arnold, Robert Dziuba, Alexander Löser
2016 International Conference on Computational Linguistics  
Our implementation captures syntactic and semantic context using a robust end-to-end LSTM sequence learner and word embeddings.  ...  Tasty supports the author of a text with complementary information about the mentioned entities shown in a 'live' exploration view.  ...  Acknowledgements Our work is funded by the Federal Ministry of Economic Affairs and Energy (BMWi) under grant agreement 01MD15010B (Project: Smart Data Web).  ... 
dblp:conf/coling/ArnoldDL16 fatcat:sbavipjaibhalboywpvgmd4cxu

Vector representations of text data in deep learning [article]

Karol Grzegorczyk
2019 arXiv   pre-print
For word-level representations we propose Disambiguated Skip-gram: a neural network model for learning multi-sense word embeddings.  ...  Representations learned by this model can be used in downstream tasks, like part-of-speech tagging or identification of semantic relations.  ...  and recommending new research directions, Professor Krzysztof Zieliński for introducing me to academia and a fellow PhD candidate Piotr Wójcik for collaboration on a few research papers.  ... 
arXiv:1901.01695v1 fatcat:et6cxs45mbcipfyvblntjwpuge

Novel Semantic Relatedness Computation for Multi-Domain Unstructured Data

Rafeeq Ahmed, Pradeep Singh, Tanvir Ahmad
2018 EAI Endorsed Transactions on Energy Web  
However, these techniques give inappropriate results for the massive multidomain dataset because they provide a relation between concepts across different domains, which are not related to each other.  ...  In this paper, a novel method, "modified Balanced Mutual Information(MBMI)," to calculate the semantic relatedness of multidomain data has been proposed.  ...  In E-learning, Learning objects with semantic similarities are used to generate a knowledge graph and recommend a personalized learning path [12, 13] .  ... 
doi:10.4108/eai.13-7-2018.165503 fatcat:wguwhksnljbvrmw6igfu7whj2i

The Technique of Different Semantic Search Engines

2020 International journal of recent technology and engineering  
, whether or not on the net to generate greater applicable result.  ...  We additionally provide a short overview of the records of semantic search and its function scope in the world.  ...  In the 1/3 step the analyzed information is matched with a thesaurus. Then semantic search accesses the matched facts and then semantic search engine returns the applicable content.. II.  ... 
doi:10.35940/ijrte.a2249.059120 fatcat:6tgbcqpj3redhirp3dajdilktu

CRCTOL: A semantic-based domain ontology learning system

Xing Jiang, Ah-Hwee Tan
2009 Journal of the American Society for Information Science and Technology  
collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule-based algorithm that extracts relations between the key concepts, and a modified generalized association  ...  As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems.  ...  We thank the anonymous reviewers for providing many invaluable comments to the previous versions of the paper. We thank Dr.  ... 
doi:10.1002/asi.21231 fatcat:ysq47lk6yjdgdin7n6j4kce374
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