Semantic Relations Between Nominals (Record no. 85570)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04396nam a22005535i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-02148-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730164323.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220607s2013 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031021480 |
| -- | 978-3-031-02148-0 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Nastase, Vivi. |
| 245 10 - TITLE STATEMENT | |
| Title | Semantic Relations Between Nominals |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2013. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XII, 107 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Human Language Technologies, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Relations between Nominals, Relations between Concepts -- Extracting Semantic Relations with Supervision -- Extracting Semantic Relations with Little or No Supervision -- Conclusion. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as "Opportunity and Curiosity find similar rocks on Mars" requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation. The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases.After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Nakov, Preslav. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Séaghdha, Diarmuid Ó. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Szpakowicz, Stan. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-02148-0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2013. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural language processing (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational linguistics. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural Language Processing (NLP). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Linguistics. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1947-4059 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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