Notes:
SimpleNLG is a natural language generation library that is written in Java. It is designed to allow developers to easily create natural language text from structured data or other input sources. SimpleNLG is commonly used in a variety of applications, such as language translation systems, chatbots, and automated summarization tools. It is designed to be easy to use and integrate into existing software systems, and can be used to generate a wide range of natural language outputs, including sentences, paragraphs, and entire documents.
SimpleNLG is designed to be efficient at generating complete sentences and other units of natural language text. The library includes a number of features and tools that are designed to make it easy to create natural language text from structured data or other input sources. For example, SimpleNLG includes a range of pre-defined grammatical rules and a lexicon of words and phrases that can be used to construct sentences and other units of text. It also includes tools for automatically generating appropriate verb conjugations and other grammatical forms based on the input data. By using these and other features of SimpleNLG, developers can create complete, grammatically correct sentences and other units of natural language text in an efficient and automated way.
It is possible to use SimpleNLG to generate an abstractive summary of a text, although doing so would likely require more than just “making some rules over relevant concepts” and applying SimpleNLG. Abstractive summarization is a complex task that involves understanding the meaning and content of a text and then generating a shortened version that conveys the most important information in a concise and coherent way. To achieve this, it is typically necessary to use a combination of natural language processing techniques, such as text understanding, language generation, and text summarization, in order to create a summary that accurately reflects the content of the original text. SimpleNLG can be used as part of this process, for example to generate natural language text from structured data or to assist with grammatical and stylistic aspects of the summary. However, it would likely need to be used in conjunction with other tools and techniques in order to create a fully functional abstractive summarization system.
It is not designed to identify or extract “relevant concepts” from text or other data on its own. Rather, it relies on the input data that is provided to it in order to generate natural language text. If the input data includes information about “relevant concepts,” such as definitions or other contextual information, then SimpleNLG can use this information to generate natural language text that includes these concepts. However, SimpleNLG itself does not have the ability to identify or extract relevant concepts from text or other data without being provided with this information as input.
SimpleNLG provides a number of interfaces that can be used to offer direct control over the way phrases are built and combined when generating natural language text. For example, the library includes a range of classes and methods that allow developers to specify the grammatical structure of a sentence, the words and phrases that should be used, and the relationships between different elements of the text. This can be useful for fine-tuning the output of a natural language generation system and ensuring that it meets the desired specifications. By using these interfaces, developers can have greater control over the way phrases are built and combined, and can create more sophisticated and accurate natural language text outputs.
SimpleNLG includes a number of features and tools that can be used to change the tense of a sentence, depending on the desired medium or context. For example, the library includes a range of pre-defined grammatical rules and a lexicon of words and phrases that can be used to construct sentences and other units of text. It also includes tools for automatically generating appropriate verb conjugations and other grammatical forms based on the input data. By using these and other features of SimpleNLG, developers can easily change the tense of a sentence when generating natural language text, depending on the desired medium or context. For example, a developer could use SimpleNLG to generate a sentence in the past tense for a news article, and then use the same library to generate a sentence in the present tense for a conversation with a chatbot.
SimpleNLG is designed to facilitate the realization of various details related to natural language generation, including agreement and conjugation. Agreement refers to the way in which words and phrases in a sentence or clause match or agree with each other in terms of their grammatical form or function. For example, in the sentence “I am going to the store,” the verb “am” agrees with the subject “I” in terms of person and number. Conjugation refers to the inflection of verbs to reflect tense, mood, or aspect. For example, in the sentence “I am going to the store,” the verb “am” is conjugated to reflect the present tense. SimpleNLG includes a range of features and tools that can be used to ensure that these and other grammatical details are properly realized when generating natural language text. For example, the library includes a set of pre-defined grammatical rules that can be used to ensure that words and phrases are correctly inflected and agree with each other, as well as a lexicon of words and phrases that can be used to construct sentences and other units of text. These and other features of SimpleNLG can be used to facilitate the realization of various grammatical details when generating natural language text.
SimpleNLG is designed to facilitate the morphological realization of words during the natural language generation process. Morphological realization refers to the process of generating well-inflected words or sentences based on the morpho-syntactic properties of the words being used. Morpho-syntactic properties refer to the way in which words are inflected or modified to reflect their grammatical role or function in a sentence or clause. For example, a verb may be conjugated to reflect the tense of the sentence, or a noun may be inflected to reflect its plural form. SimpleNLG includes a range of features and tools that can be used to facilitate the morphological realization of words during the natural language generation process. For example, the library includes a set of pre-defined grammatical rules that can be used to ensure that words and phrases are correctly inflected and agree with each other, as well as a lexicon of words and phrases that can be used to construct sentences and other units of text. By using these and other features of SimpleNLG, developers can easily generate well-inflected sentences and words during the natural language generation process.
It is possible to use SimpleNLG to generate natural language questions based on semantic role labeling annotations and the type of question. Semantic role labeling is a natural language processing task that involves identifying the roles that words or phrases play in a sentence or clause, such as subject, object, or verb. By annotating a sentence with semantic role labels, it is possible to represent the meaning and structure of the sentence in a way that can be easily understood by a computer. SimpleNLG can then be used to generate natural language text based on this annotated data, including questions. For example, if a sentence has been annotated with semantic role labels indicating the subject and verb of the sentence, SimpleNLG could be used to generate a question by reversing the subject and verb and adding a question mark at the end. This would result in a question such as “What is the subject doing?” Depending on the type of question being generated, additional steps or processing may be required in order to correctly generate the question using SimpleNLG.
SimpleNLG can be used to transform declarative sentences into questions simply by declaring the interrogative type. Declarative sentences are statements that make a statement or assertion about something. They typically include a subject and a verb, and are used to convey information or ideas. Interrogative sentences are questions that are used to ask for information or clarification. They typically include a subject and a verb, and are used to elicit a response from the listener or reader. To transform a declarative sentence into a question using SimpleNLG, you would need to provide the library with the necessary input data, such as the subject and verb of the sentence, and specify that you want to generate an interrogative sentence. SimpleNLG would then use this information and the appropriate grammatical rules to generate a natural language question. Depending on the specific requirements of your application, you may need to perform additional processing or provide additional input data in order to correctly generate the question using SimpleNLG.
The SimpleNLG API (Application Programming Interface) is a set of programming interfaces and tools that are provided as part of the SimpleNLG natural language generation library. The SimpleNLG API allows developers to easily integrate the library into their own software applications and to use its features and functionality to generate natural language text. The API includes a range of classes and methods that can be used to specify the grammatical structure of a sentence, the words and phrases that should be used, and the relationships between different elements of the text. It also includes tools for automatically generating appropriate verb conjugations and other grammatical forms based on the input data.
- Guided summarization is a type of text summarization that is based on user input or guidance. In a guided summarization system, the user may be asked to provide specific information or criteria that the system should use to generate a summary, such as the main points or key themes of the text. The system can then use this input to generate a summary that reflects the user’s desired focus or perspective.
- Language generator is a computational system that is designed to generate written or spoken language. Language generators can be used in a variety of applications, including natural language processing, machine translation, and automated summarization.
- Natural language generation (NLG) is a subfield of artificial intelligence that is concerned with the development of computational systems that are able to generate written or spoken language from non-linguistic input. NLG systems are used in a wide range of applications, including language translation systems, chatbots, automated summarization tools, and more. These systems typically use natural language processing algorithms and techniques, such as text understanding and text generation, to process and generate language from non-linguistic input, such as structured data or other machine-readable formats. The goal of NLG is to create systems that are able to generate language that is similar in style and content to human-generated language, and that is able to convey meaning and information effectively to human readers or listeners.
- Natural language generator is a type of language generator that is designed to produce written or spoken language that is similar in style and content to human-generated language. Natural language generators typically use natural language processing algorithms and techniques, such as text understanding and text generation, to produce language outputs that are coherent and meaningful to human readers or listeners.
- Realiser is a computational system that is designed to generate natural language text or speech from structured data or other input sources. Realisers typically use natural language processing algorithms and techniques, such as text understanding and text generation, to process and generate language from the input data. The goal of a realiser is to create language outputs that are similar in style and content to human-generated language and that are able to convey meaning and information.
- Realizer software is a type of computer program that is designed to generate natural language text or speech from structured data or other input sources. Realizer software typically uses natural language processing algorithms and techniques, such as text understanding and text generation, to process and generate language from the input data. The goal of realizer software is to create language outputs that are similar in style and content to human-generated language and that are able to convey meaning and information effectively to human readers or listeners.
- Realization library is a collection of software tools and resources that are designed to facilitate the process of generating natural language text or speech from structured data or other input sources. A realization library may include a range of pre-defined grammatical rules, a lexicon of words and phrases, and other resources that can be used to create natural language text.
- Sentence generator is a type of software or tool that is designed to generate complete sentences of natural language text. Sentence generators can be used for a variety of purposes, including language translation, automated summarization, and natural language processing research.
- Text generation is the process of creating written or spoken language using computational tools and techniques. Text generation systems can be used to produce a wide range of language-based outputs, including reports, summaries, responses to questions, and more. These systems typically use natural language processing algorithms and may be trained on large datasets of human-generated language in order to produce outputs that are similar in style and content to human-generated language.
- Text realizer is a term that is often used to refer to a computational system that is designed to generate natural language text from structured data or other input sources. Text realizers typically use natural language processing algorithms and techniques, such as text understanding and text generation, to process and generate language from the input data. The goal of a text realizer is to create language outputs that are similar in style and content to human-generated language and that are able to convey meaning and information effectively to human readers or listeners.
- Text-to-text generation is a type of natural language generation task that involves generating one or more pieces of written or spoken language from one or more input texts. Text-to-text generation systems are used in a wide range of applications, including language translation, automated summarization, and natural language processing research. These systems typically use natural language processing algorithms and techniques, such as text understanding and text generation, to process and generate language from the input texts. The goal of text-to-text generation is to create language outputs that are similar in style and content to human-generated language and that are able to convey meaning and information effectively to human readers or listeners.
Resources:
- arria.com .. pioneering company in the space of automatic text generation
- simplenlg .. java api for natural language generation
Wikipedia:
References:
- The story of simplenlg (2016)
- Natural Language Processing Methods for Attitudinal Near-Synonymy (2013)
See also:
KPML (Komet-Penman Multi-Lingual) | Linguistic Realizers | Natural Language Generation | NaturalOWL | OpenCCG (OpenNLP CCG Library) | Realizers In Natural Language Processing | Sentence Planner
SimpleNLG-TI: Adapting SimpleNLG to Tibetan
Z Kuanzhuo, L Lin, Z Weina – … of the 13th International Conference on …, 2020 – aclweb.org
Surface realisation is the last but not the least phase of Natural Language Generation, which aims to produce high-quality natural language text based on meaning representations. In this article, we present our work on SimpleNLG-TI, a Tibetan surface realiser, which follows …
MucLex: A German Lexicon for Surface Realisation
K Klimt, D Braun, D Schneider, F Matthes – Proceedings of The 12th …, 2020 – aclweb.org
… 2. Other Lexica SimpleNLG (Gatt and Reiter, 2009) is arguably the most popular rule-based open source surface realiser. Version 4.4.8 of SimpleNLG1 comes with a default lexicon con- taining more than 6,000 lemmata and only very little in- formation about inflection …
Arabic NLG Language Functions
W Abed, E Reiter – Proceedings of the 13th International Conference on …, 2020 – aclweb.org
… However, SimpleNLG is still considered the most popular method since it was presented by Gatt and Reiter (2009), It is a rule- based realiser that uses three main steps syntac- tic, morphological and orthographic realisations to produce a correctly inflected and high-quality …
User-adaptable Natural Language Generation for Regression Testing within the Finance Domain.
D Braun, A Sajwan, F Matthes – ICEIS (1), 2020 – daniel-braun.com
… A more detailed description of the rules and their output is given in Section 5. The NLG sys- tem itself internally follows the three-stage pipeline architecture described by Reiter and Dale (1997) and uses SimpleNLG (Gatt and Reiter, 2009) as surface realiser …
Automated Exercise Generation in Mobile Language Learning
R Verweij – 2020 – digitalcommons.bard.edu
… By contrast, the Language Lion uses a map of Dutch to English lexemes, a context-free grammar, and a modified version of the SimpleNLG sentence realizer to automatically generate semi-random translation exercises for the student …
ExpReal: a Writing Language and System for Authoring Texts in Interactive Narrative
N Szilas, R De Jong, M Theune – … on the Foundations of Digital Games, 2020 – dl.acm.org
… 2 RELATED WORK SimpleNLG is a widely used surface realizer, originally for English [8], that has been adapted to multiple languages and offers users direct control over the realization process, including operations such as word ordering and inflection …
To what extent does content selection affect surface realization in the context of headline generation?
C Barros, M Vicente, E Lloret – Computer Speech & Language, 2021 – Elsevier
… In contrast to other well-known surface realization approaches, such as SimpleNLG (Gatt and Reiter, 2009), HanaNLG does not need a well formatted input and it can generate a sentence on his own. This is an added value …
Verbalizing the Evolution of Knowledge Graphs with Formal Concept Analysis
MA Riveros, M Tasnim, D Graux, F Orlandi… – 2020 – ceur-ws.org
… 3 KGs. One of the most representative works here is SimpleNLG [10] and its differ- ent variants, French, Spanish, German, and Italian. SimpleNLG defines a three-stage pipeline for Natural Language Generation (NLG) … We propose to use SimpleNLG [10] …
Verbalizing the Evolution of Knowledge Graphs with Formal Concept Analysis
M Arispe, M Tasnim, D Graux, F Orlandi, D Collarana – 2020 – researchgate.net
… 3 KGs. One of the most representative works here is SimpleNLG [10] and its differ- ent variants, French, Spanish, German, and Italian. SimpleNLG defines a three-stage pipeline for Natural Language Generation (NLG) … We propose to use SimpleNLG [10] …
Proceedings of the 13th International Conference on Natural Language Generation
B Davis, Y Graham, J Kelleher, Y Sripada – Proceedings of the 13th …, 2020 – aclweb.org
… 80 SimpleNLG-TI: Adapting SimpleNLG to Tibetan Zewang Kuanzhuo, Li Lin and Zhao Weina … SimpleNLG-TI: Adapting SimpleNLG to Tibetan Zewang Kuanzhuo, Li Lin and Zhao Weina …
NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu
J Shabbir, MU Arshad, W Shahzad – arXiv preprint arXiv:2102.10410, 2021 – arxiv.org
… A. API based NLG systems 1) SimpleNLG:: SimpleNLG is a library with Java API that generates sentences after the subject-verb and the subjects are described. The Library follows the API approach to sentence making and words must be transmitted into the Java System …
Italian Counter Narrative Generation to Fight Online Hate Speech
YL Chung, SS Tekiroglu, M Guerini – 2020 – ceur-ws.org
… For instance, there is the port- ing of SimpleNLG API (Gatt and Reiter, 2009) to Dutch (de Jong and Theune, 2018) and Italian (Mazzei et al., 2016), or Bilingual generation via combining NMT and Generative Adversarial Net- works (Rashid et al., 2019) …
Ontogen: A Knowledge-Based Approach to Natural Language Generation
IE Leon – 2020 – search.proquest.com
… 9 3.1 Parts of speech supported by SimpleNLG. . . . . 23 iii Page 5. LIST OF FIGURES 2.1 The OntoAgent Architecture … 22 3.10 Step 4: SimpleNLG class mappings. . . . . 24 …
Evaluation rules! On the use of grammars and rule-based systems for NLG evaluation
E Van Miltenburg, C van der Lee, TC Ferreira… – Proceedings of the 1st …, 2020 – aclweb.org
… Feasibility A natural question at this point is how to find ex- isting systems to generate synthetic NLG corpora. One answer is simply to look for systems using SimpleNLG, since this is probably the most used realisation engine for NLG in academia …
The jsRealB Text Realizer: Organization and Use Cases
G Lapalme – arXiv preprint arXiv:2012.15425, 2020 – arxiv.org
… sentences produced by a Prolog program taking input from AMR structures [10] or from Universal Dependencies in the context of the Surface Realization Shared Task (SR’19) at EMNLP [9] and for the WebNLG Challenge [3]. jsRealB was strongly influenced by SimpleNLG …
An explainable Link Discovery: Multilingual Link Specification verbalization and summarization
AF Ahmed, MA Sherif, D Moussallem… – Data & Knowledge …, 2021 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …
Teaching Explainable Artificial Intelligence to High School Students
JM Alonso – International Journal of Computational Intelligence …, 2020 – atlantis-press.com
Artificial Intelligence (AI) is part of our everyday life and has become one of the most outstanding and strategic technologies. Explainable AI (XAI) is expected to endow intelligent systems with fairness, accountability, transparency and explanation ability when interacting with humans …
Someone really wanted that song but it was not me!
S Najafian, O Inel, N Tintarev – 2020 – research.tudelft.nl
… Surface realization. To allow us to dynamically and automati- cally change the generated explanations we used the SimpleNLG2 library for realizing natural language … [2] Albert Gatt and Ehud Reiter. 2009. SimpleNLG: A realisation engine for practical applications …
Contribution to Natural Language Generation for Spanish
S Garcia Mendez – 2021 – investigo.biblioteca.uvigo.es
… Firstly, we introduce the adaptation of the popular SimpleNLG library to Spanish and an en- hanced version of it with automatic performance which expands text from keywords … Capítulo 7. Adaptación de SimpleNLG al castellano y versión mejorada …
Content Selection for Explanation Requests in Customer-Care Domain
L Anselma, MF Di Lascio, D Mana, A Mazzei… – 2nd Workshop on …, 2020 – iris.unito.it
… In this linguis- tically sound NLG architecture, we use a simple rule-based sentence planner (Anselma and Mazzei, 2018) in combination with the Italian version of SimpleNLG (Mazzei et al., 2016) for generating messages that give emphasis and priority to the content elements …
Building a Persuasive Virtual Dietitian
L Anselma, A Mazzei – Informatics, 2020 – mdpi.com
… Similar to MADiMan, (i) some persuasive strategies inspired by captology (computer as persuasive technology [7]) were used and (ii) a complete linguistically-sound NLG pipeline was applied; moreover, (iii) the SimpleNLG realizer was used …
Generating Explanations of Action Failures in a Cognitive Robotic Architecture
R Thielstrom, A Roque, M Chita-Tegmark… – 2nd Workshop on …, 2020 – aclweb.org
… The gram- matical signifiers are used to assign grammatical structure as needed, which are then conjugated and fully realized using SimpleNLG (Gatt and Reiter, 2009) into natural language, such as: “I cannot prepare the product because to prepare something I must weigh it …
Applying Machine Learning to the Task of Generating Search Queries
A Gusenkov, A Sittikova – 2020 – ceur-ws.org
… Transformer model. There are text generation tools based on these methods, for example commercial Arria NLG PLC, AX Semantics, Yseop and others, as well as open source programs Simplenlg, GPT, GPT-2, BERT, XLNet. Also …
Fluent Response Generation for Conversational Question Answering
A Baheti, A Ritter, K Small – arXiv preprint arXiv:2005.10464, 2020 – arxiv.org
… The remaining questions are processed via the following transformations to over-generate a list of candidate answers: (1) Verb modifica- tion: change the tense of the main verb based on the auxiliary verb using SimpleNLG (Gatt and Re- iter, 2009); (2) Pronoun replacement …
Re-generating sentences from Universal Dependencies structures
G Lapalme – 2020 – rali.iro.umontreal.ca
… 5 Page 6. 2.2 jsRealB jsRealB[5] is a surface realizer written in Javascript similar in principle to SimpleNLG [1] in which programming language instructions create data structures corresponding to the constituents of the sentence to be produced …
Abstractive Web News Summarization Using Knowledge Graphs
M Lakshika, HA Caldera… – 2020 20th International …, 2020 – ieeexplore.ieee.org
… knowledge graph. In the phase 4, abstractive summaries are generated using the SimpleNLG language generator. Updated abstractive summaries are generated for an existing abstractive summary. Generated correlations …
Someone really wanted that song but it was not me! Evaluating Which Information to Disclose in Explanations for Group Recommendations
S Najafian, O Inel, N Tintarev – … of the 25th International Conference on …, 2020 – dl.acm.org
… Surface realization. To allow us to dynamically and automati- cally change the generated explanations we used the SimpleNLG2 library for realizing natural language … [2] Albert Gatt and Ehud Reiter. 2009. SimpleNLG: A realisation engine for practical applications …
A Deep Dive into Supervised Extractive and Abstractive Summarization from Text
M Dey, D Das – Data Visualization and Knowledge Engineering, 2020 – Springer
… Text generation is implemented using the SimpleNLG realizer, whose input is a sentence with each words root from and output is a sentence with markers. One drawback of using this method is the effort taken to manually write all the rules. 4 Semantic Approaches …
Argument-based plan explanation
N Oren, K van Deemter, WW Vasconcelos – Knowledge Engineering Tools …, 2020 – Springer
… In some cases, NLG can be accomplished via a simple language realisation toolkit such as SimpleNLG [12], or using template-based techniques as in [8]. This approach works well for the information in our running example (eg, the bottom left window in Fig. 9.3) …
Computer Assisted Natural Language Description of Trends and Patterns in Time Series Data
S Kandarkar – wwwmatthes.in.tum.de
Page 1. DEPARTMENT OF INFORMATICS TECHNISCHE UNIVERSITÄT MÜNCHEN Master’s Thesis in Data Engineering and Analytics Computer Assisted Natural Language Description of Trends and Patterns in Time Series Data Siddhesh Kandarkar Page 2 …
Spatial relation learning for explainable image classification and annotation in critical applications
R Pierrard, JP Poli, C Hudelot – Artificial Intelligence, 2021 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …
RDFJSREALB: a Symbolic Approach for Generating Text from RDF Triples
G Lapalme – 2020 – rali.iro.umontreal.ca
… Document structuring consists in decid- ing the order of output: first classes, then attributes and finally relationship information. SimpleNLG (Gatt and Reiter, 2009) is used for creating the English text. Our system follows a similar approach. Vougiouklis et al …
A probabilistic conversational agent for intelligent tutoring systems
T Sosnowski, K Yordanova – Proceedings of the 13th ACM International …, 2020 – dl.acm.org
… representations is provided by [8], and a system concept is described by [7]. The question answering and generation of explanations from a graph-like knowledge model have been discussed by [5]. Here, natural language generation is done using SimpleNLG toolkit [10] …
Template-Based Natural Language Generation in Interpreting Laboratory Blood Test.
OS Sitompul, EB Nababan, D Arisandi, I Aulia… – … International Journal of …, 2021 – iaeng.org
… nurses’ structural documentation data). Subsequently, the selected information extracted from the graph was written into a summary text. The last process undertaken was the application of SimpleNLG system. A similar research in …
Development of a privacy-by-design speech assistant providing nutrient information for German seniors
A Seiderer, H Ritschel, E André – Proceedings of the 6th EAI International …, 2020 – dl.acm.org
… From Rasa we also used the integrated NLG al- though it is currently quite limited. A more sophisticated option is for example SimpleNLG [11] or RosaeNLG13, which provide Ger- man language models … 2009. SimpleNLG: A realisation engine for practical applications …
OWLSIZ: An isiZulu CNL for structured knowledge validation
Z Mahlaza, CM Keet – Proceedings of the 3rd International Workshop on …, 2020 – aclweb.org
… 2016). They either use bare templates or SimpleNLG (Gatt and Reiter, 2009) for realisation, where ‘bare’ tem- plates are a sequence of fixed words and slot, such as, Model-T’s template (Puzikov and Gurevych, 2018). None …
Automated Text Generation Driven By Data
MSPDF Khosmood – marshallplan.at
Page 1. Klimashevskaia Anastasiia, Bsc Automated Text Generation Driven By Data Marshall Plan Scientific Report submitted to Austrian Marshall Plan Foundation Supervisor Assoc.Prof. Dipl.-Ing. Dr.techn. Christian Gütl Institute of Interactive Systems and Data Science …
The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance
B Youngmann, E Yom-Tov, R Gilad-Bachrach… – Proceedings of The …, 2020 – dl.acm.org
Page 1. The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance Brit Youngmann Microsoft Research Herzliya, Israel t-bryoun@microsoft.com Elad Yom-Tov Microsoft Research Herzliya, Israel eladyt@microsoft …
Natural Language Generation
C Room – algorithms, 2020 – devopedia.org
… Among the commercial tools are Arria NLG , AX Semantics, Yseop, Quill, and Wordsmith. Among the open source tools are SimpleNLG and NaturalOWL. Wordsmith is from Automated Insights, who can be regarded as a pioneer in NLG …
Addressing Regulatory Requirements on Explanations for Automated Decisions with Provenance—A Case Study
TD Huynh, N Tsakalakis, A Helal… – … : Research and Practice, 2021 – dl.acm.org
… 2009. SimpleNLG: A realisation engine for practical applications … 12 We also recorded the full provenance of the loan decision pipeline, which is provided in the Supplementary Materials. 13 We use the SimpleNLG library [8] as the NLG engine in the demonstrator …
Interactive Natural Language Technology for Human-centric Explainable Artificial Intelligence
JM Alonso – researchgate.net
… Crisp DT: J48, REPTree, RandomTree ? Fuzzy DT: FHDT ? Fuzzy Rules: FURIA ? Textual + Visual Explanations ? Textual: Natural Language (simpleNLG) ? Visual: Trees, Rules, FINGRAMS ? Global + Local Explanations ? Global: Confusion Matrix …
Sell-Bot: An Intelligent Tool for Advertisement Synthesis on Social Media
S Kabaso, A Ade-Ibijola – The Disruptive Fourth Industrial Revolution, 2020 – Springer
… 6. Simplenlg: This is an open source NLG API written in JAVA originally developed by Ehud Reiter. It is an easy-to-us library and provides clear documentation for businesses or individuals that what to create NLG applications (Gatt and Reiter 2009). 7 …
AUTOMATIC GENERATION OF TEXT FOR MATCH RECAPS USING ESPORT CASTER COMMENTARIES
O Olarewaju, AV Kokkinakis, S Demediuk… – jonhook.co.uk
… used to generate the text. Finally, we used simpleNLG [25] an off the self NLG toolkit to create and enforce all linguistic rules for the final text that describes the moment of highlight. 4. CORPUS CREATION In the absence of naturally …
Novel model to integrate word embeddings and syntactic trees for automatic caption generation from images
H Zhang, D Qiu, R Wu, D Ji, G Li, Z Niu, T Li – Soft Computing, 2020 – Springer
Automatic caption generation from images is an interesting and mainstream direction in the field of machine learning. This method enables us to build a pow.
Generation and Evaluation of Factual and Gounterfaetual Explanations for Decision Trees and Fuzzy Rule-based Classifiers
I Stepin, JM Alonso, A Gatala… – … Conference on Fuzzy …, 2020 – ieeexplore.ieee.org
… In addition, the package SimpleNLG [34] was used to implement the NLG surface realization module. For the sake of reproducibility, all the source code and complementary materials are made available online [35]. IV. EVALUATION …
Towards Harnessing Natural Language Generation to Explain Black-box Models
E Mariotti, JM Alonso, A Gatt – 2nd Workshop on Interactive Natural …, 2020 – aclweb.org
… ArXiv:1806.00340. A. Gatt and E. Reiter. 2009. SimpleNLG: A Realisa- tion Engine for Practical Applications. In Proceed- ings of the 12th European Workshop on Natural Lan- guage Generation (ENLG), pages 90–93, Athens, Greece. Association for Computational Linguistics …
Generating Explanations in Natural Language from Knowledge Graphs
I Tiddi – Knowledge Graphs for eXplainable Artificial …, 2020 – books.google.com
… RDF2PT operates mostly at the level of the first two and to the Realization task, RDF2PT uses an adaption of SimpleNLG to Brazilian Portuguese [17] … To this end, we perform this step by relying on a Brazilian adaptation of SimpleNLG [17] and [44]. 3.1 …
Natural language generation in dialogue systems for customer care
MF Di Lascio, M Sanguinetti, L Anselma… – … Italian Conference on …, 2020 – iris.unito.it
… 2016. SimpleNLG-IT: adapting Sim- pleNLG to Italian. In Proceedings of the 9th Inter- national Natural Language Generation conference, pages 184–192, Edinburgh, UK, September 5-8. As- sociation for Computational Linguistics …
Knowledge Engineering with Semantic Technologies to Identify and Warn of Transport Disruptions
D Corsar, M Markovic, P Edwards, P Gault, C Cottrill… – semantic-web-journal.net
Page 1. Semantic Web 0 (0) 1 1 IOS Press 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 …
Talking Realities: Audio Guides in Virtual Reality Visualizations
S Latif, H Tarner, F Beck – IEEE Computer Graphics and …, 2021 – ieeexplore.ieee.org
… interaction. To ensure the correct grammar and to handle grammatical tasks (eg, subject–verb agreement, pluralization, etc.), we leverage SimpleNLG—a realization engine for generating sentences from their syntactic form …
A new approach for extracting the conceptual schema of texts based on the linguistic Thematic Progression theory
E del Olmo, AM Fernández-Pampillón – 2020 – ceur-ws.org
… 26 Page 5. REFERENCES 1. A. Gatt and E. Reiter, ‘SimpleNLG: A realisation engine for practical applications’, Proceedings of the 12th European Workshop on Natural Language Generation, 90-93, (2009). 2. A. Templeton and J. Kalita …
Natural language processing-enhanced extraction of SBVR business vocabularies and business rules from UML use case diagrams
P Danenas, T Skersys, R Butleris – Data & Knowledge Engineering, 2020 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …
TOWARDS AUTOMATIC GENERATION OF EXPLANATION OF ANALOGIES AT VARIOUS LEVELS OF COMPREHENSIVENESS
S Keshwani, A Chakrabarti – 2020 – researchgate.net
… We suspect the following reasons that reduced the accuracy of support: a) use of rule-based approach; b) sequential steps due to which errors in one step led to errors in the following steps; c) accuracy of the algorithms in SimpleNLG and Ollie- the two tools used in this work …
A semantic-aware video auto-captioning method
LT Tin – 2020 – eprints.utar.edu.my
… Figure 2.1 Human High-Level Features analysed from hand annotations The paper stated uses template-based methods for sentence generation (SVO triplets) by using simpleNLG by (Albert Gatt, 2009). However, for each HLFs extracted from …
A new approach for extracting the conceptual schema of texts based on the linguistic Thematic Progression theory
EO Suárez, AMFP Cesteros – arXiv preprint arXiv:2010.07440, 2020 – arxiv.org
… R. Page 6. REFERENCES [1] Gatt and E. Reiter, ‘SimpleNLG: A realisation engine for practical applications’, Proceedings of the 12th European Workshop on NLG, 90-93, (2009). [2] A. Templeton and J. Kalita. ‘Exploring Sentence …
Automated Java exceptions explanation using natural language generation techniques
FY Assiri, H Elazhary – Computer Applications in Engineering …, 2020 – Wiley Online Library
… Libraries exist for this purpose. For example, SimpleNLG [11] is a Java library that provides interfaces for facilitating the realisation process. 5.7 Structure realisation. In this step, we structure the document and add all the needed symbols …
A Text Reassembling Approach to NaturalLanguage Generation
X Li, K van Deemter, C Lin – arXiv preprint arXiv:2005.07988, 2020 – arxiv.org
Page 1. Natural Language Engineering 1 (1): 1–30. Printed in the United Kingdom © 1998 Cambridge University Press 1 A Text Reassembling Approach to Natural Language Generation Xiao Li, Kees van Deemter, and Chenghua Lin ( Received date 1; Revised date 2) …
Stan: Towards describing bytecodes of smart contract
X Li, T Chen, X Luo, T Zhang, L Yu… – 2020 IEEE 20th …, 2020 – ieeexplore.ieee.org
Page 1. STAN: Towards Describing Bytecodes of Smart Contract Xiaoqi Li ? , Ting Chen † , Xiapu Luo ?¶ , Tao Zhang ‡ , Le Yu ? , Zhou Xu ?§ ? Department of Computing, The Hong Kong Polytechnic University, Hong Kong …
Building natural language responses from natural language questions in the spatio-temporal context
G Landoulsi, K Mahmoudi… – International Journal of …, 2021 – inderscienceonline.com
… Finally, the realisation can be carried out. For this task we used the SimpleNLG developer, which is a Java API to create a syntactic text structure. Figure 19 ST-SQL query generation from the STuq (see online version for colours) Page 19 …
Knowledge Graphs for Multilingual Language Translation and Generation
D Moussallem – arXiv preprint arXiv:2009.07715, 2020 – arxiv.org
Page 1. DOCTORAL DISSERTATION Knowledge Graphs for Multilingual Language Translation and Generation A dissertation presented by Diego Campos Moussallem to the Faculty for Computer Science, Electrical Engineering and Mathematics of Paderborn University …
Dialogue policies for learning board games through multimodal communication
M Zare, A Ayub, A Liu, S Sudhakara, A Wagner… – Proceedings of the 21th …, 2020 – aclweb.org
Page 1. Proceedings of the SIGdial 2020 Conference, pages 339–351 1st virtual meeting, 01-03 July 2020. c 2020 Association for Computational Linguistics 339 Dialogue Policies for Learning Board Games through Multimodal Communication …
Deep Learning for Text Attribute Transfer: A Survey
D Jin, Z Jin, R Mihalcea – arXiv preprint arXiv:2011.00416, 2020 – arxiv.org
Page 1. Deep Learning for Text Attribute Transfer: A Survey Di Jin? MIT CSAIL jindi15@mit.edu Zhijing Jin* Max Planck Institute zjin@tuebingen.mpg.de Rada Mihalcea University of Michigan mihalcea@umich.edu Abstract …
Deep Learning for Text Style Transfer: A Survey
D Jin, Z Jin, Z Hu, O Vechtomova, R Mihalcea – zhijing-jin.com
Page 1. Deep Learning for Text Style Transfer: A Survey Di Jin? MIT CSAIL jindi15@mit.edu Zhijing Jin* Max Planck Institute zjin@tuebingen.mpg.de Zhiting Hu UC San Diego zhh019@ucsd.edu Olga Vechtomova University of Waterloo ovechtom@uwaterloo.ca …
Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing
P Danenas, T Skersys, R Butleris – Applied Sciences, 2020 – mdpi.com
Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to …
An algorithm to generate short sentences in natural language from linked open data based on linguistic templates
ALD Silva, SJ Rigo… – International Journal of …, 2020 – inderscienceonline.com
Page 1. Int. J. Metadata Semantics and Ontologies, Vol. 14, No. 3, 2020 197 Copyright © 2020 Inderscience Enterprises Ltd. An algorithm to generate short sentences in natural language from linked open data based on linguistic templates …
Formalisation and classification of grammar and template-mediated techniques to model and ontology verbalisation
Z Mahlaza, CM Keet – International Journal of Metadata …, 2020 – inderscienceonline.com
Page 1. Int. J. Metadata Semantics and Ontologies, Vol. 14, No. 3, 2020 249 Copyright © 2020 Inderscience Enterprises Ltd. Formalisation and classification of grammar and template-mediated techniques to model and ontology verbalisation Zola Mahlaza* and C. Maria Keet …
Deep Architectures for Visual Recognition and Description
A Perunninakulath Parameshwaran – 2020 – scholarworks.gsu.edu
Page 1. Georgia State University ScholarWorks @ Georgia State University Computer Science Dissertations Department of Computer Science Summer 8-11-2020 Deep Architectures for Visual Recognition and Description Anuja Perunninakulath Parameshwaran …
Neural Methods for Sentiment Analysis and Text Summarization
TH Le – 2020 – hal.univ-lorraine.fr
Page 1. HAL Id: tel-02929745 https://hal.univ-lorraine.fr/tel-02929745 Submitted on 3 Sep 2020 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not …
Detection of Financial Opportunities in Micro-Blogging Data With a Stacked Classification System
F De Arriba-Pérez, S García-Méndez… – IEEE …, 2020 – ieeexplore.ieee.org
Page 1. Received October 31, 2020, accepted November 24, 2020, date of publication November 27, 2020, date of current version December 11, 2020. Digital Object Identifier 10.1109/ACCESS. 2020.3041084 Detection of Financial Opportunities in Micro-Blogging Data With a …