FrameNet CNL: a Knowledge Representation and Information Extraction Language

The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL. The framework is used on natural language documents and represents the extracted knowledge in a tailor-made Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be generated automatically in multiple languages. This approach brings together the fields of information extraction and CNL, because a source text can be considered belonging to FrameNet-CNL, if information extraction parser produces the correct knowledge representation as a result. We describe a state-of-the-art information extraction parser used by a national news agency and speculate that FrameNet-CNL eventually could shape the natural language subset used for writing the newswire articles.Comment: CNL-2014 camera-ready version. The final publication is available at link.springer.co

Projecting named entity tags from a resource rich language to a resource poor language

Named Entities (NE) are the prominent entities appearing in textual documents.Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc.This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism.A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism.The English corpus is the translated version of the Malay corpus.Hence, these two corpora are treated as parallel corpora. The method computes the string similarity between the English words and the list of available lexemes in a pre-built lexicon that approximates the best NE mapping.The algorithm has been effectively evaluated using our own terrorism tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.An evaluation of the selected open source NER tool for English is also presented

Towards Multilingual Coreference Resolution

The current work investigates the problems that occur when coreference resolution is considered as a multilingual task. We assess the issues that arise when a framework using the mention-pair coreference resolution model and memory-based learning for the resolution process are used. Along the way, we revise three essential subtasks of coreference resolution: mention detection, mention head detection and feature selection. For each of these aspects we propose various multilingual solutions including both heuristic, rule-based and machine learning methods. We carry out a detailed analysis that includes eight different languages (Arabic, Catalan, Chinese, Dutch, English, German, Italian and Spanish) for which datasets were provided by the only two multilingual shared tasks on coreference resolution held so far: SemEval-2 and CoNLL-2012. Our investigation shows that, although complex, the coreference resolution task can be targeted in a multilingual and even language independent way. We proposed machine learning methods for each of the subtasks that are affected by the transition, evaluated and compared them to the performance of rule-based and heuristic approaches. Our results confirmed that machine learning provides the needed flexibility for the multilingual task and that the minimal requirement for a language independent system is a part-of-speech annotation layer provided for each of the approached languages. We also showed that the performance of the system can be improved by introducing other layers of linguistic annotations, such as syntactic parses (in the form of either constituency or dependency parses), named entity information, predicate argument structure, etc. Additionally, we discuss the problems occurring in the proposed approaches and suggest possibilities for their improvement

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Promocijas darbs

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

Semantic approaches to domain template construction and opinion mining from natural language

Most of the text mining algorithms in use today are based on lexical representation of input texts, for example bag of words. A possible alternative is to first convert text into a semantic representation, one that captures the text content in a structured way and using only a set of pre-agreed labels. This thesis explores the feasibility of such an approach to two tasks on collections of documents: identifying common structure in input documents (»domain template construction«), and helping users find differing opinions in input documents (»opinion mining«). We first discuss ways of converting natural text to a semantic representation. We propose and compare two new methods with varying degrees of target representation complexity. The first method, showing more promise, is based on dependency parser output which it converts to lightweight semantic frames, with role fillers aligned to WordNet. The second method structures text using Semantic Role Labeling techniques and aligns the output to the Cyc ontology.\ud Based on the first of the above representations, we next propose and evaluate two methods for constructing frame-based templates for documents from a given domain (e.g. bombing attack news reports). A template is the set of all salient attributes (e.g. attacker, number of casualties, \ldots). The idea of both methods is to construct abstract frames for which more specific instances (according to the WordNet hierarchy) can be found in the input documents. Fragments of these abstract frames represent the sought-for attributes. We achieve state of the art performance and additionally provide detailed type constraints for the attributes, something not possible with competing methods. Finally, we propose a software system for exposing differing opinions in the news. For any given event, we present the user with all known articles on the topic and let them navigate them by three semantic properties simultaneously: sentiment, topical focus and geography of origin. The result is a dynamically reranked set of relevant articles and a near real time focused summary of those articles. The summary, too, is computed from the semantic text representation discussed above. We conducted a user study of the whole system with very positive results

What Ukraine Taught NATO about Hybrid Warfare

Russia’s invasion of Ukraine in 2022 forced the United States and its NATO partners to be confronted with the impact of hybrid warfare far beyond the battlefield. Targeting Europe’s energy security, Russia’s malign influence campaigns and malicious cyber intrusions are affecting global gas prices, driving up food costs, disrupting supply chains and grids, and testing US and Allied military mobility. This study examines how hybrid warfare is being used by NATO’s adversaries, what vulnerabilities in energy security exist across the Alliance, and what mitigation strategies are available to the member states. Cyberattacks targeting the renewable energy landscape during Europe’s green transition are increasing, making it urgent that new tools are developed to protect these emerging technologies. No less significant are the cyber and information operations targeting energy security in Eastern Europe as it seeks to become independent from Russia. Economic coercion is being used against Western and Central Europe to stop gas from flowing. China’s malign investments in Southern and Mediterranean Europe are enabling Beijing to control several NATO member states’ critical energy infrastructure at a critical moment in the global balance of power. What Ukraine Taught NATO about Hybrid Warfare will be an important reference for NATO officials and US installations operating in the European theater.https://press.armywarcollege.edu/monographs/1952/thumbnail.jp

Semantic approaches to domain template construction and opinion mining from natural language

Most of the text mining algorithms in use today are based on lexical representation of input texts, for example bag of words. A possible alternative is to first convert text into a semantic representation, one that captures the text content in a structured way and using only a set of pre-agreed labels. This thesis explores the feasibility of such an approach to two tasks on collections of documents: identifying common structure in input documents (»domain template construction«), and helping users find differing opinions in input documents (»opinion mining«). We first discuss ways of converting natural text to a semantic representation. We propose and compare two new methods with varying degrees of target representation complexity. The first method, showing more promise, is based on dependency parser output which it converts to lightweight semantic frames, with role fillers aligned to WordNet. The second method structures text using Semantic Role Labeling techniques and aligns the output to the Cyc ontology. Based on the first of the above representations, we next propose and evaluate two methods for constructing frame-based templates for documents from a given domain (e.g. bombing attack news reports). A template is the set of all salient attributes (e.g. attacker, number of casualties, \ldots). The idea of both methods is to construct abstract frames for which more specific instances (according to the WordNet hierarchy) can be found in the input documents. Fragments of these abstract frames represent the sought-for attributes. We achieve state of the art performance and additionally provide detailed type constraints for the attributes, something not possible with competing methods. Finally, we propose a software system for exposing differing opinions in the news. For any given event, we present the user with all known articles on the topic and let them navigate them by three semantic properties simultaneously: sentiment, topical focus and geography of origin. The result is a dynamically reranked set of relevant articles and a near real time focused summary of those articles. The summary, too, is computed from the semantic text representation discussed above. We conducted a user study of the whole system with very positive results