1,115 research outputs found

    Unifying context with labeled property graph: A pipeline-based system for comprehensive text representation in NLP

    Get PDF
    Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed approach introduces specialized patterns and algorithms to enhance specific NLP tasks, including nominal mention detection, named entity disambiguation, event enrichments, event participant detection, and temporal link detection. The evaluation of the proposed approach, using the MEANTIME corpus comprising manually annotated documents, provides encouraging results and valuable insights into the system\u27s strengths. The proposed pipeline-based framework serves as a solid foundation for future research, aiming to refine and optimize LPG-based graph structures to generate comprehensive and semantically rich text representations, addressing the challenges associated with efficient information extraction and analysis in NLP

    Comparing the production of a formula with the development of L2 competence

    Get PDF
    This pilot study investigates the production of a formula with the development of L2 competence over proficiency levels of a spoken learner corpus. The results show that the formula in beginner production data is likely being recalled holistically from learners’ phonological memory rather than generated online, identifiable by virtue of its fluent production in absence of any other surface structure evidence of the formula’s syntactic properties. As learners’ L2 competence increases, the formula becomes sensitive to modifications which show structural conformity at each proficiency level. The transparency between the formula’s modification and learners’ corresponding L2 surface structure realisations suggest that it is the independent development of L2 competence which integrates the formula into compositional language, and ultimately drives the SLA process forward

    Promocijas darbs

    Get PDF
    Elektroniskā versija nesatur pielikumusPromocijas darbs veltīts hibrīda latviešu valodas gramatikas modeļa izstrādei un transformēšanai uz Universālo atkarību (Universal Dependencies, UD) modeli. Promocijas darbā ir aizsākts jauns latviešu valodas izpētes virziens – sintaktiski marķētos tekstos balstīti pētījumi. Darba rezultātā ir izstrādāts un aprobēts fundamentāls, latviešu valodai iepriekš nebijis valodas resurss – mašīnlasāms sintaktiski marķēts korpuss 17 tūkstošu teikumu apmērā. Teikumi ir marķēti atbilstoši diviem dažādiem sintaktiskās marķēšanas modeļiem – darbā radītajam frāžu struktūru un atkarību gramatikas hibrīdam un starptautiski aprobētajam UD modelim. Izveidotais valodas resurss publiski pieejams gan lejuplādei, gan tiešsaistes meklēšanai abos iepriekš minētajos marķējuma veidos. Pētījuma laikā radīta rīku kopa un latviešu valodas sintaktiski marķētā korpusa veidošanai vajadzīgā infrastruktūra. Tajā skaitā tika definēti plašam valodas pārklājumam nepieciešamie LU MII eksperimentālā hibrīdā gramatikas modeļa paplašinājumi. Tāpat tika analizētas iespējas atbilstoši hibrīdmodelim marķētus datus pārveidot uz atkarību modeli, un tika radīts atvasināts UD korpuss. Izveidotais sintaktiski marķētais korpuss ir kalpojis par pamatu, lai varētu radīt augstas precizitātes (91%) parsētājus latviešu valodai. Savukārt dalība UD iniciatīvā ir veicinājusi latviešu valodas un arī citu fleksīvu valodu resursu starptautisko atpazīstamību un fleksīvām valodām piemērotāku rīku izveidi datorlingvistikā – pētniecības jomā, kuras vēsturiskā izcelsme pamatā meklējama darbā ar analītiskajām valodām. Atslēgvārdi: sintakses korpuss, Universal Dependencies, valodu tehnoloģijasThe given doctoral thesis describes the creation of a hybrid grammar model for the Latvian language, as well as its subsequent conversion to a Universal Dependencies (UD) grammar model. The thesis also lays the groundwork for Latvian language research through syntactically annotated texts. In this work, a fundamental Latvian language resource was developed and evaluated for the first time – a machine-readable treebank of 17 thousand syntactically annotated sentences. The sentences are annotated according to two syntactic annotation models: the hybrid grammar model developed in the thesis, and the internationally recognised UD model. Both annotated versions of the treebank are publicly available for downloading or querying online. Over the course of the study, a set of tools and infrastructure necessary for treebank creation and maintenance were developed. The language coverage of the IMCS UL experimental hybrid model was extended, and the possibilities were defined for converting data annotated according to the hybrid grammar model to the dependency grammar model. Based on this work, a derived UD treebank was created. The resulting treebank has served as a basis for the development of high accuracy (91%) Latvian language parsers. Furthermore, the participation in the UD initiative has promoted the international recognition of Latvian and other inflective languages and the development of better-fitted tools for inflective language processing in computational linguistics, which historically has been more oriented towards analytic languages. Keywords: treebank, Universal Dependencies, language technologie

    Advances in automatic terminology processing: methodology and applications in focus

    Get PDF
    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The information and knowledge era, in which we are living, creates challenges in many fields, and terminology is not an exception. The challenges include an exponential growth in the number of specialised documents that are available, in which terms are presented, and the number of newly introduced concepts and terms, which are already beyond our (manual) capacity. A promising solution to this ‘information overload’ would be to employ automatic or semi-automatic procedures to enable individuals and/or small groups to efficiently build high quality terminologies from their own resources which closely reflect their individual objectives and viewpoints. Automatic terminology processing (ATP) techniques have already proved to be quite reliable, and can save human time in terminology processing. However, they are not without weaknesses, one of which is that these techniques often consider terms to be independent lexical units satisfying some criteria, when terms are, in fact, integral parts of a coherent system (a terminology). This observation is supported by the discussion of the notion of terms and terminology and the review of existing approaches in ATP presented in this thesis. In order to overcome the aforementioned weakness, we propose a novel methodology in ATP which is able to extract a terminology as a whole. The proposed methodology is based on knowledge patterns automatically extracted from glossaries, which we considered to be valuable, but overlooked resources. These automatically identified knowledge patterns are used to extract terms, their relations and descriptions from corpora. The extracted information can facilitate the construction of a terminology as a coherent system. The study also aims to discuss applications of ATP, and describes an experiment in which ATP is integrated into a new NLP application: multiplechoice test item generation. The successful integration of the system shows that ATP is a viable technology, and should be exploited more by other NLP applications

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

    Get PDF
    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    SDbQfSum: Query-focused summarization framework basedon diversity and text semantic analysis

    Get PDF
    Query-focused multi-document summarization (Qf-MDS) is a sub-task of automatic text summarization that aims to extract a substitute summary from a document cluster of the same topic and based on a user query. Unlike other summarization tasks, Qf-MDS has specific research challenges including the differences and similarities across related document sets, the high degree of redundancy inherent in the summaries created from multiple related sources, relevance to the given query, topic diversity in the produced summary and the small source-to-summary compression ratio. In this work, we propose a semantic diversity feature based query-focused extractive summarizer (SDbQfSum) built on powerful text semantic representation techniques underpinned with Wikipedia commonsense knowledge in order to address the query-relevance, centrality, redundancy and diversity challenges. Specifically, a semantically parsed document text is combined with knowledge-based vectorial representation to extract effective sentence importance and query-relevance features. The proposed monolingual summarizer is evaluated on a standard English dataset for automatic query-focused summarization tasks, that is, the DUC2006 dataset. The obtained results show that our summarizer outperforms most state-of-the-art related approaches on one or more ROUGE measures achieving 0.418, 0.092 and 0.152 in ROUGE-1, ROUGE-2,and ROUGE-SU4 respectively. It also attains competitive performance with the slightly outperforming system(s), for example, the difference between our system's result and best system in ROUGE-1 is just 0.006. We also found through the conducted experiments that our proposed custom cluster merging algorithm significantly reduces information redundancy while maintaining topic diversity across documents
    corecore