60 research outputs found

    Towards a universal index of meaning

    Get PDF
    The Inter-Lingual-Index (ILI) in the EuroWordNet architecture is an initially unstructured fund of concepts which functions as the link between the various language wordnets.The ILI concepts originate from WordNet1.5, and have been restructured on the basis of aspects of the internal structure of Word-Net,links between WordNet and other resources,and multilingual mapping between the wordnets. This leads to a differentiation of the status of ILI concepts,a reduction of the Wordnet polysemy,and a greater connectivity between the wordnets. The restructured ILI represents the first step towards a standardized set of word meanings,is a working platform for further development and testing,and can be put to use in NLP tasks such as (multilingual)information retrieval

    Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines

    Get PDF
    Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF

    Graph-based methods for large-scale multilingual knowledge integration

    Get PDF
    Given that much of our knowledge is expressed in textual form, information systems are increasingly dependent on knowledge about words and the entities they represent. This thesis investigates novel methods for automatically building large repositories of knowledge that capture semantic relationships between words, names, and entities, in many different languages. Three major contributions are made, each involving graph algorithms and statistical techniques that combine evidence from multiple sources of information. The lexical integration method involves learning models that disambiguate word meanings based on contextual information in a graph, thereby providing a means to connect words to the entities that they denote. The entity integration method combines semantic items from different sources into a single unified registry of entities by reconciling equivalence and distinctness information and solving a combinatorial optimization problem. Finally, the taxonomic integration method adds a comprehensive and coherent taxonomic hierarchy on top of this registry, capturing how different entities relate to each other. Together, these methods can be used to produce a large-scale multilingual knowledge base semantically describing over 5 million entities and over 16 million natural language words and names in more than 200 different languages.Da ein großer Teil unseres Wissens in textueller Form vorliegt, sind Informationssysteme in zunehmendem Maße auf Wissen über Wörter und den von ihnen repräsentierten Entitäten angewiesen. Gegenstand dieser Arbeit sind neue Methoden zur automatischen Erstellung großer multilingualer Wissensbanken, welche semantische Beziehungen zwischen Wörtern bzw. Namen und Konzepten bzw. Entitäten formal erfassen. In drei Hauptbeiträgen werden jeweils graphtheoretische bzw. statistische Verfahren zur Verknüpfung von Indizien aus mehreren Wissensquellen vorgestellt. Bei der lexikalischen Integration werden statistische Modelle zur Disambiguierung gebildet. Die Entitäten-Integration fasst semantische Einheiten unter Auflösung von Konflikten zwischen Äquivalenz- und Verschiedenheitsinformationen zusammen. Diese werden schließlich bei der taxonomischen Integration durch eine umfassende taxonomische Hierarchie ergänzt. Zusammen können diese Methoden zur Induzierung einer großen multilingualen Wissensbank eingesetzt werden, welche über 5 Millionen Entitäten und über 16 Millionen Wörter und Namen in mehr als 200 Sprachen semantisch beschreibt

    Lexical database enrichment through semi-automated morphological analysis

    Get PDF
    Derivational morphology proposes meaningful connections between words and is largely unrepresented in lexical databases. This thesis presents a project to enrich a lexical database with morphological links and to evaluate their contribution to disambiguation. A lexical database with sense distinctions was required. WordNet was chosen because of its free availability and widespread use. Its suitability was assessed through critical evaluation with respect to specifications and criticisms, using a transparent, extensible model. The identification of serious shortcomings suggested a portable enrichment methodology, applicable to alternative resources. Although 40% of the most frequent words are prepositions, they have been largely ignored by computational linguists, so addition of prepositions was also required. The preferred approach to morphological enrichment was to infer relations from phenomena discovered algorithmically. Both existing databases and existing algorithms can capture regular morphological relations, but cannot capture exceptions correctly; neither of them provide any semantic information. Some morphological analysis algorithms are subject to the fallacy that morphological analysis can be performed simply by segmentation. Morphological rules, grounded in observation and etymology, govern associations between and attachment of suffixes and contribute to defining the meaning of morphological relationships. Specifying character substitutions circumvents the segmentation fallacy. Morphological rules are prone to undergeneration, minimised through a variable lexical validity requirement, and overgeneration, minimised by rule reformulation and restricting monosyllabic output. Rules take into account the morphology of ancestor languages through co-occurrences of morphological patterns. Multiple rules applicable to an input suffix need their precedence established. The resistance of prefixations to segmentation has been addressed by identifying linking vowel exceptions and irregular prefixes. The automatic affix discovery algorithm applies heuristics to identify meaningful affixes and is combined with morphological rules into a hybrid model, fed only with empirical data, collected without supervision. Further algorithms apply the rules optimally to automatically pre-identified suffixes and break words into their component morphemes. To handle exceptions, stoplists were created in response to initial errors and fed back into the model through iterative development, leading to 100% precision, contestable only on lexicographic criteria. Stoplist length is minimised by special treatment of monosyllables and reformulation of rules. 96% of words and phrases are analysed. 218,802 directed derivational links have been encoded in the lexicon rather than the wordnet component of the model because the lexicon provides the optimal clustering of word senses. Both links and analyser are portable to an alternative lexicon. The evaluation uses the extended gloss overlaps disambiguation algorithm. The enriched model outperformed WordNet in terms of recall without loss of precision. Failure of all experiments to outperform disambiguation by frequency reflects on WordNet sense distinctions

    Lexical database enrichment through semi-automated morphological analysis

    Get PDF
    Derivational morphology proposes meaningful connections between words and is largely unrepresented in lexical databases. This thesis presents a project to enrich a lexical database with morphological links and to evaluate their contribution to disambiguation. A lexical database with sense distinctions was required. WordNet was chosen because of its free availability and widespread use. Its suitability was assessed through critical evaluation with respect to specifications and criticisms, using a transparent, extensible model. The identification of serious shortcomings suggested a portable enrichment methodology, applicable to alternative resources. Although 40% of the most frequent words are prepositions, they have been largely ignored by computational linguists, so addition of prepositions was also required. The preferred approach to morphological enrichment was to infer relations from phenomena discovered algorithmically. Both existing databases and existing algorithms can capture regular morphological relations, but cannot capture exceptions correctly; neither of them provide any semantic information. Some morphological analysis algorithms are subject to the fallacy that morphological analysis can be performed simply by segmentation. Morphological rules, grounded in observation and etymology, govern associations between and attachment of suffixes and contribute to defining the meaning of morphological relationships. Specifying character substitutions circumvents the segmentation fallacy. Morphological rules are prone to undergeneration, minimised through a variable lexical validity requirement, and overgeneration, minimised by rule reformulation and restricting monosyllabic output. Rules take into account the morphology of ancestor languages through co-occurrences of morphological patterns. Multiple rules applicable to an input suffix need their precedence established. The resistance of prefixations to segmentation has been addressed by identifying linking vowel exceptions and irregular prefixes. The automatic affix discovery algorithm applies heuristics to identify meaningful affixes and is combined with morphological rules into a hybrid model, fed only with empirical data, collected without supervision. Further algorithms apply the rules optimally to automatically pre-identified suffixes and break words into their component morphemes. To handle exceptions, stoplists were created in response to initial errors and fed back into the model through iterative development, leading to 100% precision, contestable only on lexicographic criteria. Stoplist length is minimised by special treatment of monosyllables and reformulation of rules. 96% of words and phrases are analysed. 218,802 directed derivational links have been encoded in the lexicon rather than the wordnet component of the model because the lexicon provides the optimal clustering of word senses. Both links and analyser are portable to an alternative lexicon. The evaluation uses the extended gloss overlaps disambiguation algorithm. The enriched model outperformed WordNet in terms of recall without loss of precision. Failure of all experiments to outperform disambiguation by frequency reflects on WordNet sense distinctions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Evaluation and Improvement of Semantically-Enhanced Tagging System

    Get PDF
    The Social Web or ‘Web 2.0’ is focused on the interaction and collaboration between web sites users. It is credited for the existence of tagging systems, amongst other things such as blogs and Wikis. Tagging systems like YouTube and Flickr offer their users the simplicity and freedom in creating and sharing their own contents and thus folksonomy is a very active research area where many improvements are presented to overcome existing disadvantages such as the lack of semantic meaning, ambiguity, and inconsistency. TE is a tagging system proposing solutions to the problems of multilingualism, lack of semantic meaning and shorthand writing (which is very common in the social web) through the aid of semantic and social resources. The current research is presenting an addition to the TE system in the form of an embedded stemming component to provide a solution to the different lexical form problems. Prior to this, the TE system had to be explored thoroughly and then its efficiency had to be determined in order to decide on the practicality of embedding any additional components as enhancements to the performance. Deciding on this involved analysing the algorithm efficiency using an analytical approach to determine its time and space complexity. The TE had a time growth rate of O (N²) which is polynomial, thus the algorithm is considered efficient. Nonetheless, recommended modifications like patch SQL execution can improve this. Regarding space complexity, the number of tags per photo represents the problem size which, if it grows, will increase linearly the required memory space. Based on the findings above, the TE system is re-implemented on Flickr instead of YouTube, because of a recent YouTube restriction, which is of greater benefit in multi languages tagging system since the language barrier is meaningless in this case. The re-implementation is achieved using ‘flickrj’ (Java Interface for Flickr APIs). Next, the stemming component is added to perform tags normalisation prior to the ontologies querying. The component is embedded using the Java encoding of the porter 2 stemmer which support many languages including Italian. The impact of the stemming component on the performance of the TE system in terms of the size of the index table and the number of retrieved results is investigated using an experiment that showed a reduction of 48% in the size of the index table. This also means that search queries have less system tags to compare them against the search keywords and this can speed up the search. Furthermore, the experiment runs similar search trails on two versions of the TE systems one without the stemming component and the other with the stemming component and found out that the latter produced more results on the conditions of working with valid words and valid stems. The embedding of the stemming component in the new TE system has lessened the effect of the storage overhead needed for the generated system tags by their reduction for the size of the index table which make the system suited for many applications such as text classification, summarization, email filtering, machine translation…etc

    Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

    Get PDF
    Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

    An Urdu semantic tagger - lexicons, corpora, methods and tools

    Get PDF
    Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as Natural Language Processing (NLP), corpus linguistics, data sciences, etc. An important aspect of such automatic information extraction and analysis is the semantic annotation of language data using semantic annotation tool (a.k.a semantic tagger). Generally, different semantic annotation tools have been designed to carry out various levels of semantic annotations, for instance, sentiment analysis, word sense disambiguation, content analysis, semantic role labelling, etc. These semantic annotation tools identify or tag partial core semantic information of language data, moreover, they tend to be applicable only for English and other European languages. A semantic annotation tool that can annotate semantic senses of all lexical units (words) is still desirable for the Urdu language based on USAS (the UCREL Semantic Analysis System) semantic taxonomy, in order to provide comprehensive semantic analysis of Urdu language text. This research work report on the development of an Urdu semantic tagging tool and discuss challenging issues which have been faced in this Ph.D. research work. Since standard NLP pipeline tools are not widely available for Urdu, alongside the Urdu semantic tagger a suite of newly developed tools have been created: sentence tokenizer, word tokenizer and part-of-speech tagger. Results for these proposed tools are as follows: word tokenizer reports F1F_1 of 94.01\%, and accuracy of 97.21\%, sentence tokenizer shows F1_1 of 92.59\%, and accuracy of 93.15\%, whereas, POS tagger shows an accuracy of 95.14\%. The Urdu semantic tagger incorporates semantic resources (lexicon and corpora) as well as semantic field disambiguation methods. In terms of novelty, the NLP pre-processing tools are developed either using rule-based, statistical, or hybrid techniques. Furthermore, all semantic lexicons have been developed using a novel combination of automatic or semi-automatic approaches: mapping, crowdsourcing, statistical machine translation, GIZA++, word embeddings, and named entity. A large multi-target annotated corpus is also constructed using a semi-automatic approach to test accuracy of the Urdu semantic tagger, proposed corpus is also used to train and test supervised multi-target Machine Learning classifiers. The results show that Random k-labEL Disjoint Pruned Sets and Classifier Chain multi-target classifiers outperform all other classifiers on the proposed corpus with a Hamming Loss of 0.06\% and Accuracy of 0.94\%. The best lexical coverage of 88.59\%, 99.63\%, 96.71\% and 89.63\% are obtained on several test corpora. The developed Urdu semantic tagger shows encouraging precision on the proposed test corpus of 79.47\%
    corecore