RuleCNL: A Controlled Natural Language for Business Rule Specifications

Business rules represent the primary means by which companies define their business, perform their actions in order to reach their objectives. Thus, they need to be expressed unambiguously to avoid inconsistencies between business stakeholders and formally in order to be machine-processed. A promising solution is the use of a controlled natural language (CNL) which is a good mediator between natural and formal languages. This paper presents RuleCNL, which is a CNL for defining business rules. Its core feature is the alignment of the business rule definition with the business vocabulary which ensures traceability and consistency with the business domain. The RuleCNL tool provides editors that assist end-users in the writing process and automatic mappings into the Semantics of Business Vocabulary and Business Rules (SBVR) standard. SBVR is grounded in first order logic and includes constructs called semantic formulations that structure the meaning of rules.Comment: 12 pages, 7 figures, Fourth Workshop on Controlled Natural Language (CNL 2014) Proceeding

Measuring the Justice Gap: Flaws in the Interstate Allocation of Civil Legal Services Funding and a Proposed Remedy

With the supply of legal services not particularly responsive to demand, we conclude that the justice gap could be narrowed simply by reforming the way in which policymakers distribute legal services funds while holding constant the total amount of funds distributed. In reaching this conclusion, we proceed in two parts. First, drawing largely from Access Across America and LSC data, we analyze the supply of legal services funding across states. Since eligibility for Legal Services Corporation (LSC) funds is principally determined by income (only individuals in households with income at or below 125% of the federal poverty level are LSC eligible),8 variations in legal services funding among states are strongly correlated with LSC eligibility levels. However, LSC funding likely accounts for well under forty-three percent (43%) of overall legal services funding, with the remainder (“non-LSC funding”) generated by, inter alia, state and local grants, filing fees, interest on lawyer trust accounts (“IOLTA”), and private grants. Because the precise magnitude of non-LSC funding is unclear, we estimate it with three different measures. Using each of these measures, we then analyze its disparity among states. In every case, after explaining Access Across America’s finding that non-LSC funding is not proportional to population, we conclude that it also has no statistically significant relationship to key economic indicators, such as LSC eligibility, median household income, or unemployment. In fact, of the variables we tested, only the number of lawyers in a state relates significantly to any of our measures of non-LSC funding, and of these three measures, the only one for which the number of lawyers has statistical significance is non-LSC funding received by organizations that also receive LSC funding. After examining how legal services funds are supplied across states, we then analyze how they are demanded. Measuring demand is quite challenging, particularly on the state level, because it requires assessing not the amount of legal services that low-income individuals do use, but rather the amount that they want to use, which is an unobservable variable. The LSC has attempted to measure such demand through a survey of individuals seeking assistance from LSC-funded programs, but, as the LSC concedes, this approach comes with inherent limitations that likely under-represent unmet needs. We therefore take a different approach: after assuming that the overall frequency with which civil legal services are delivered reflects the relative demand for these services across states, we estimate demand within each state through proxies for the most significant categories of services. Because, according to LSC data, nearly eighty-five percent (85%) of LSC-eligible cases arise from just four types of disputes (consumer finance, family, housing, and income), we can reasonably project state-level demand for legal services by estimating the frequency of these disputes within each state. Upon doing so, we find that there is no clear connection between state-level demand and supply, particularly with respect to LSC funding. In other words, states with the greatest need for LSC funding (because their residents encounter legal problems the most based on our estimates) do not necessarily have more funding than states with lower funding needs. Though we recognize that fixing this imbalance will not be easy, we conclude by offering a proposal that attempts to do so. In this regard, we recommend that the LSC move away from complete reliance on an income-based test toward a needs-based test. Such a framework would allow the LSC to more effectively serve unmet demand for civil legal services and thus, help realize Justice Powell’s ideal

The fall of Joseph McCarthy, 1953-1954: a descriptive and theoretical account

Call number: LD2668 .T4 1967 C615Master of Scienc

What's Policy Got to Do With It? Cultural Policy and Forms of Writing

The article examines the rhetoric of the Keating government's Creative Nation and compares it with the fiction of Nicholas Jose in The Custodians and the ficto-criticism of Stephen Muecke in No Road

Indirectly Named Entity Recognition

[EN] We define here indirectly named entities, as a term to denote multiword expressions referring to known named entities by means of periphrasis.  While named entity recognition is a classical task in natural language processing, little attention has been paid to indirectly named entities and their treatment. In this paper, we try to address this gap, describing issues related to the detection and understanding of indirectly named entities in texts. We introduce a proof of concept for retrieving both lexicalised and non-lexicalised indirectly named entities in French texts. We also show example cases where this proof of concept is applied, and discuss future perspectives. We have initiated the creation of a first lexicon of 712 indirectly named entity entries that is available for future research.This research has been funded by the FEDER (Fonds européen de développement régional) and selected by the French-Swiss programme Interreg V. We would like to thank Claire Wuillemin for her preliminary work in the DecRIPT project about the State-of-the-Art in NER and SER in 2020. We would also like to thank for their advice Gilles Falquet, Luka Nerima, Eric Wehrli and Jean-Philippe Goldman at the University of Geneva.Kauffmann, A.; Rey, F.; Atanassova, I.; Gaudinat, A.; Greenfield, P.; Madinier, H.; Cardey, S. (2021). Indirectly Named Entity Recognition. Journal of Computer-Assisted Linguistic Research. 5(1):27-46. https://doi.org/10.4995/jclr.2021.15922OJS274651Abney, Steven. 1987. "The English Noun Phrase in its Sentential Aspect." PhD diss., Massachusetts Institute of Technology.Alsharaf, H., S. Cardey, P. Greenfield, D. Limame, and I. Skouratov. 2003. "Fixedness, the complexity and fragility of the phenomenon: some solutions for natural language processing." In Proceedings of ICL17. Prague, Czech Republic: Matfyzpress.Ananthanarayanan, Rema, Vijil Chenthamarakshan, Prasad M Deshpande, and Raghuram Krishnapuram. 2008. "Rule Based Synonyms for Entity Extraction from Noisy Text." In Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data AND '08, 31-38. Singapore: Association for Computing Machinery. https://doi.org/10.1145/1390749.1390756Bachellier, Jean-Louis. 1972. "Sur-Nom." Le texte: de la théorie à la recherche, no. 19: 69-92. doi :10.3406/comm.1972.1283. https://doi.org/10.3406/comm.1972.1283Baldwin, Timothy, and Su Nam Kim. 2013. "Multiword Expressions." In Handbook of Natural Language Processing, Second Edition, edited by Nitin Indurkhya and Fred J. Damerau, 267-292. Boca Raton, USA: CRCPress.Bohn, C., and Kjeti Nørvag. 2010. "Extracting Named Entities and Synonyms from Wikipedia." In Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, 1300-1307. https://doi.org/10.1109/AINA.2010.50Cai, Desheng, and Gongqing Wu. 2019. "Content-aware attributed entity embedding for synonymous named entity discovery." Neurocomputing 329: 237-247. https://doi.org/10.1016/j.neucom.2018.10.055Chakrabarti, K., S. Chaudhuri, T. Cheng, and Dong Xin. 2012. "A framework for robust discovery of entity synonyms." In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1384-1392, Beijing, China: Association for Computing Machinery. https://doi.org/10.1145/2339530.2339743Charton, Eric, Michel Gagnon, and Benoit Ozell. 2011. "Génération automatique de motifs de détection d'entités nommées en utilisant des contenus encyclopédiques (Automatic generation of named entity detection patterns using encyclopedic contents)" [in French]. In Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs, 13-24. Montpellier, France: ATALA.Cho, Hyejin, Wonjun Choi, and Hyunju Lee. 2017. "A method for named entity normalization in biomedical articles: application to diseases and plants." BMC bioinformatics 18, no. 1 ( 1-12. https://doi.org/10.1186/s12859-017-1857-8Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186. Minneapolis, Minnesota: Association for Computational Linguistics.Friburger, Nathalie. 2006. "Linguistique et reconnaissance automatique des noms propres." Meta 51, no. 4: 637-650. doi:10.7202/014331ar. https://doi.org/10.7202/014331arGuenoune, Hani, Kevin Cousot, Mathieu Lafourcade, Melissa Mekaoui, and Cédric Lopez. 2020. "A Dataset for Anaphora Analysis in French Emails." In Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference, 165-175. Barcelona, Spain (online): Association for Computational Linguistics.Honnibal, Matthew, and Ines Montani. 2017. "spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing."Kampeera, Wannachai, and Sylviane Cardey-Greenfield. 2012. "Building a Lexically and Semantically-Rich Resource for Paraphrase Processing." In Advances in Natural Language Processing, edited by Hitoshi Isahara and Kyoko Kanzaki, 138-143. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_14Kauffmann, Alexis. 2013. "Structural Asymmetries in Machine Translation: The case of English-Japanese". PhD diss., Université de Genève. https://doi.org/10.13097/archive-ouverte/unige:34540.Lample, Guillaume, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. "Neural Architectures for Named Entity Recognition." In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 260-270. San Diego, California: Association for Computational Linguistics. https://doi.org/10.18653/v1/N16-1030Lin, Bill Yuchen, Dong-Ho Lee, M. Shen, Ryan Rene Moreno, X. Huang, Prashant Shiralkar, and X. Ren. 2020. "TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8503-8511. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.752Lopez, C., Melissa Mekaoui, K. Aubry, Jean Bort, and Philippe Garnier. 2019. "Reconnaissance d'entités nommées itérative sur une structure en dépendances syntaxiques avec l'ontologie NERD." Revue des Nouvelles Technologies de l'Information, Extraction et Gestion des connaissances, RNTI-E-35, 81-92.Ma, Jie, Jun Liu, Y. Li, X. Hu, Yudai Pan, S. Sun, and Qika Lin. 2020. "Jointly Optimized Neural Coreference Resolution with Mutual Attention." In Proceedings of the 13th International Conference on Web Search and Data Mining. Houston, Texas, USA: Association for Computing Machinery. https://doi.org/10.1145/3336191.3371787Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60. Baltimore, Maryland: Association for Computational Linguistics. https://doi.org/10.3115/v1/P14-5010Martin, Louis, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Benoıt Sagot, and Djamé Seddah. 2020. "Les modèles de langue contextuels CamemBERT pour le français: impact de la taille et de l'hétérogénéité des données d'entrainement (CamemBERT Contextual Language Models for French: Impact of Training Data Size and Heterogeneity)" [in French]. In Actes de la 6e conférence conjointe Journées d'Etudes sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Etudiants Chercheurs en Informatique pour le' Traitement Automatique des Langues (RECITAL, 22e édition). Volume 2: Traitement Automatique des Langues Naturelles, 54-65. Nancy, France: ATALA et AFCP.Mitkov, Ruslan. 2014. Anaphora resolution. Routledge. https://doi.org/10.4324/9781315840086Mohamed, Muhidin A., and Mourad Chabane Oussalah. 2020. "A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics." Language Resources and Evaluation 54 : 457-485. https://doi.org/10.1007/s10579-019-09466-4Nadeau, David, and Satoshi Sekine. 2007. "A survey of named entity recognition and classification." Lingvisticae Investigationes 30: 3-26. https://doi.org/10.1075/li.30.1.03nadNayel, Hamada A., H. L. Shashirekha, Hiroyuki Shindo, and Yuji Matsumoto. 2019. "Improving Multi-Word Entity Recognition for Biomedical Texts." CoRRabs/1908.05691. arXiv:1908.05691.Nebhi, Kamel. 2013. "Named Entity Disambiguation using Freebase and Syntactic Parsing." In [email protected], Damien, Maud Ehrmann, and Sophie Rosset. 2016. "Evaluating Named Entity Recognition." Chap. 6 in Named Entities for Computational Linguistics, 111-129. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119268567.ch6Ortiz Suarez, Pedro Javier, Yoann Dupont, Benjamin Muller, Laurent Romary, and Benoıt Sagot. 2020. "Establishing a New State-of-the-Art for French Named Entity Recognition" [in English]. In Proceedings of the 12th Language Resources and Evaluation Conference, 4631-4638. Marseille, France: European Language Resources Association.Petit, Gérard. 2006. "Le nom de marque déposée : nom propre, nom commun et terme." Meta 51, no. 4: 690-705. doi:10.7202/014335ar. https://doi.org/10.7202/014335arQu, Meng, Xiang Ren, and Jiawei Han. 2017. "Automatic Synonym Discovery with Knowledge Bases." In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 997-1005. KDD '17. Halifax, NS, Canada: Association for Computing Machinery. https://doi.org/10.1145/3097983.3098185Racicot, André. 2009. "Traduire le monde: Venise du Nord et autres surnoms." L'Actualité langagière, vol. 6, n° 2, 23. Travaux publics et Services gouvernementaux Canada.Rey, François-Claude, and Kauffmann Alexis. 2021. "French indirectly named entities (version 1.3) [Data set]." Zenodo. https://doi.org/10.5281/zenodo.5158253.Rosales-Méndez, Henry, Aidan Hogan, and Barbara Poblete. 2019. "Fine-Grained Evaluation for Entity Linking." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 718-727. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1066Sales, Juliano Efson, André Freitas, Brian Davis, and Siegfried Handschuh. 2016. "A Compositional-Distributional Semantic Model for Searching Complex Entity Categories." In Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, 199-208. Berlin, Germany: Association for Computational Linguistics. https://doi.org/10.18653/v1/S16-2025Schmitt, X., S. Kubler, J. Robert, M. Papadakis, and Y. LeTraon. 2019. "A Replicable Comparison Study of NER Software: StanfordNLP, NLTK, OpenNLP, SpaCy, Gate." In Proceedings of the Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), 338-343. https://doi.org/10.1109/SNAMS.2019.8931850Shang, Jingbo, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren, and Jiawei Han. 2018. "Learning Named Entity Tagger using Domain-Specific Dictionary." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2054-2064. Brussels, Belgium: Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1230Shen, Jiaming, Ruiliang Lyu, Xiang Ren, Michelle Vanni, Brian Sadler, and Jiawei Han. 2019. "Mining entity synonyms with efficient neural set generation." In Proceedings of the AAAI Conference on Artificial Intelligence, 33:249-256. doi:10.1609/aaai.v33i01.3301249. https://doi.org/10.1609/aaai.v33i01.3301249Shinyama, Yusuke, Satoshi Sekine, and Kiyoshi Sudo. 2002. "Automatic Paraphrase Acquisition from News Articles." In Proceedings of the Second International Conference on Human Language Technology Research, 313-318. HLT '02. San Diego, California: Morgan Kaufmann Publishers Inc. https://doi.org/10.3115/1289189.1289218Sjöblom, Paula. 2016. "Commercial names." Chap. V.31 in The Oxford Handbook of Names and Naming, edited by Carole Hough, 453-464. Oxford, UK: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199656431.013.56Tenney, Ian, Dipanjan Das, and Ellie Pavlick. 2019. "BERT Rediscovers the Classical NLP Pipeline." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 4593-4601. Florence, Italy: Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1452Treps, Marie. 2012. La rançon de la gloire - Les surnoms de nos politiques. Paris, France: Editions du Seuil.Watanabe, Taiki, Akihiro Tamura, Takashi Ninomiya, Takuya Makino, and Tomoya Iwakura. 2019. "Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 6244-6249. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1648Wehrli, Eric, and Luka Nerima. 2018. "Anaphora resolution, collocations and translation." In Multiword units in machine translation and translation technology, edited by Johanna Monti, Violeta Seretan, Gloria Corpas Pastor, and Ruslan Mitkov, 244-256. John Benjamins. https://doi.org/10.1075/cilt.341.12wehWehrli, Eric, Violeta Seretan, and Luka Nerima. 2010. "Sentence Analysis and Collocation Identification." In Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications, 28-36. Beijing, China: Coling 2010 Organizing Committee.Weston, L., V. Tshitoyan, J. Dagdelen, O. Kononova, A. Trewartha, K. A. Persson, G. Ceder, and A. Jain. 2019. "Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature." Journal of Chemical Information and Modeling 59, no. 9: 3692-3702. doi: 10.1021/acs.jcim.9b00470. https://doi.org/10.1021/acs.jcim.9b00470Wu, G., Y. He, and X. Hu. 2018. "Entity Linking: An Issue to Extract Corresponding Entity With Knowledge Base." IEEE Access 6: 6220-6231. doi:10.1109/ACCESS.2017.2787787. https://doi.org/10.1109/ACCESS.2017.2787787Yang, Yiying, Xi Yin, Haiqin Yang, Xingjian Fei, Hao Peng, Kaijie Zhou, Kunfeng Lai, and Jianping Shen. 2021. "KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph." In Database Systems for Advanced Applications, edited by Christian S. Jensen, Ee-Peng Lim, De-Nian Yang, Wang-Chien Lee, Vincent S. Tseng, Vana Kalogeraki, Jen-Wei Huang, and Chih-Ya Shen, 174-190. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-73194-6_13Zhang, Ruoyu, Wenpeng Lu, Shoujin Wang, Xueping Peng, Rui Yu, and Yuan Gao. 2021. "Chinese clinical named entity recognition based on stacked neural network." Concurrency and Computation: Practice and Experience : 33:e5775. doi:10.1002/cpe.5775. https://doi.org/10.1002/cpe.577

Operation Program for the Spatially Phase-Shifted Digital Speckle Pattern Interferometer - SPS-DSPI

SPS-DSPI software has been revised so that Goddard optical engineers can operate the instrument, instead of data programmers. The user interface has been improved to view the data collected by the SPS-DSPI, with a real-time mode and a play-back mode. The SPS-DSPI has been developed by NASA/GSFC to measure the temperature distortions of the primary-mirror backplane structure for the James Webb Space Telescope. It requires a team of computer specialists to run successfully, because, at the time of this reporting, it just finished the prototype stage. This software improvement will transition the instrument to become available for use by many programs that measure distortio

Developing employability in engineering education: a systematic review of the literature

In this systematic review of the research literature on engineering employability, curricular and pedagogical arrangements that prepare graduates for work in the twenty-first century were identified. The research question guiding the review was: Which curricular and pedagogical arrangements promote engineering students’ employability? The particular focus of the study was on how authors prioritised engineering knowledge and professional skills. The review drew on a theoretical framework that differentiated between engineering knowledge and professional skills to explain how employability could be included in engineering programmes. Data was obtained from research studies over the period 2007–2017. We found an interdependent relationship between engineering knowledge and professional skills that enabled engineering graduates to attain employability. The com of engineering problems require students to master engineering knowledge, while the ability to work with others across contexts requires professional skills. Both are necessary for deep understanding of engineering principles and a focus on real world problem