The paper investigation for Universal dependencies for Korean Language with How to convert for Finnish.
- paper seminar for conversion to universal dependencies, which is for How to convert to universal dependencies in Korean.
Building Universal Dependency Treebanks in KoreanJinho Choi
This paper presents three treebanks in Korean that consist of dependency trees derived from existing treebanks, the Google UD Treebank, the Penn Korean Treebank, and the KAIST Treebank, and pseudo-annotated by the latest guidelines from the Universal Dependencies (UD) project. The Korean portion of the Google UD Treebank is re-tokenized to match the morpheme-level annotation suggested by the other corpora, and systematically assessed for errors. Phrase structure trees in the Penn Korean Treebank and the KAIST Treebank are automatically converted into dependency trees using head-finding rules and linguistic heuristics. Additionally, part-of-speech tags in all treebanks are converted into the UD tagset. A total of 38K+ dependency trees are generated that comprise a coherent set of dependency relations for over a half million tokens. To the best of our knowledge, this is the first time that these Korean corpora are analyzed together and transformed into dependency trees following the latest UD guidelines, version 2.
An Extensible Multilingual Open Source LemmatizerCOMRADES project
Ahmet Aker and Johann Petraka and Firas Sabbahb
Department of Computer Science, University of Sheffield
Department of Information Engineering, University of Duisburg-Essen
a.aker@is.inf.uni-due.de, johann.petrak@sheffield.ac.uk
firas.sabbah@stud.uni-due.de
Building Universal Dependency Treebanks in KoreanJinho Choi
This paper presents three treebanks in Korean that consist of dependency trees derived from existing treebanks, the Google UD Treebank, the Penn Korean Treebank, and the KAIST Treebank, and pseudo-annotated by the latest guidelines from the Universal Dependencies (UD) project. The Korean portion of the Google UD Treebank is re-tokenized to match the morpheme-level annotation suggested by the other corpora, and systematically assessed for errors. Phrase structure trees in the Penn Korean Treebank and the KAIST Treebank are automatically converted into dependency trees using head-finding rules and linguistic heuristics. Additionally, part-of-speech tags in all treebanks are converted into the UD tagset. A total of 38K+ dependency trees are generated that comprise a coherent set of dependency relations for over a half million tokens. To the best of our knowledge, this is the first time that these Korean corpora are analyzed together and transformed into dependency trees following the latest UD guidelines, version 2.
An Extensible Multilingual Open Source LemmatizerCOMRADES project
Ahmet Aker and Johann Petraka and Firas Sabbahb
Department of Computer Science, University of Sheffield
Department of Information Engineering, University of Duisburg-Essen
a.aker@is.inf.uni-due.de, johann.petrak@sheffield.ac.uk
firas.sabbah@stud.uni-due.de
Near Duplicate Document Detection: Mathematical Modeling and AlgorithmsLiwei Ren任力偉
Near-duplicate document detection is a well-known problem in the area of information retrieval. It is an important problem to be solved for many applications in IT industry. It has been studied with profound research literatures. This article provides a novel solution to this classic problem. We present the problem with abstract models along with additional concepts such as text models, document fingerprints and document similarity. With these concepts, the problem can be transformed into keyword like search problem with results ranked by document similarity. There are two major techniques. The first technique is to extract robust and unique fingerprints from a document. The second one is to calculate document similarity effectively. Algorithms for both fingerprint extraction and document similarity calculation are introduced as a complete solution.
A deep analysis of Multi-word Expression and Machine TranslationLifeng (Aaron) Han
A deep analysis of Multi-word Expression and Machine Translation. Faculty research open day. DCU, Dublin. 2019.
Including MWE identification, MT with radical, MTE.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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Call us at : 08263069601
Author Credits - Maaz Nomani
A Proposition Bank is a collection of sentences which are hand-annotated with the information of semantic labels in the respective sentences. Currently, around 10,000 sentences containing 0.2 million words have been hand-annotated with the semantic labels information.
This is a natural language resource of very rich linguistic information which can be used in a variety of NLP applications such as semantic parsing, syntactic parsing, sentiment analysis, dialogue systems etc.
In this paper, we present one such resource for a resource-poor Indian language Urdu. The Proposition Bank of Urdu is built on already built Treebank of Urdu (A Treebank is a corpus of sentences annotated with their POS, morphological, head, TAM and dependency labels information). A Propbank adds a layer of semantic information over this Treebank and hence can facilitate semantic parsing and other semantic level operations in a natural language sentence.
Corpus annotation for corpus linguistics (nov2009)Jorge Baptista
Lecture on corpus annotation for corpus linguistics. Contents: DIY corpus, e-texts, character set and text encoding issues, document structure, DTDs, documentation;
tools and issues in annotation procedures, good practices; examples from anaphora resolution and named entity recognition annotation campaigns; evaluation of corpus annotation
Near Duplicate Document Detection: Mathematical Modeling and AlgorithmsLiwei Ren任力偉
Near-duplicate document detection is a well-known problem in the area of information retrieval. It is an important problem to be solved for many applications in IT industry. It has been studied with profound research literatures. This article provides a novel solution to this classic problem. We present the problem with abstract models along with additional concepts such as text models, document fingerprints and document similarity. With these concepts, the problem can be transformed into keyword like search problem with results ranked by document similarity. There are two major techniques. The first technique is to extract robust and unique fingerprints from a document. The second one is to calculate document similarity effectively. Algorithms for both fingerprint extraction and document similarity calculation are introduced as a complete solution.
A deep analysis of Multi-word Expression and Machine TranslationLifeng (Aaron) Han
A deep analysis of Multi-word Expression and Machine Translation. Faculty research open day. DCU, Dublin. 2019.
Including MWE identification, MT with radical, MTE.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Author Credits - Maaz Nomani
A Proposition Bank is a collection of sentences which are hand-annotated with the information of semantic labels in the respective sentences. Currently, around 10,000 sentences containing 0.2 million words have been hand-annotated with the semantic labels information.
This is a natural language resource of very rich linguistic information which can be used in a variety of NLP applications such as semantic parsing, syntactic parsing, sentiment analysis, dialogue systems etc.
In this paper, we present one such resource for a resource-poor Indian language Urdu. The Proposition Bank of Urdu is built on already built Treebank of Urdu (A Treebank is a corpus of sentences annotated with their POS, morphological, head, TAM and dependency labels information). A Propbank adds a layer of semantic information over this Treebank and hence can facilitate semantic parsing and other semantic level operations in a natural language sentence.
Corpus annotation for corpus linguistics (nov2009)Jorge Baptista
Lecture on corpus annotation for corpus linguistics. Contents: DIY corpus, e-texts, character set and text encoding issues, document structure, DTDs, documentation;
tools and issues in annotation procedures, good practices; examples from anaphora resolution and named entity recognition annotation campaigns; evaluation of corpus annotation
Author credits - Maaz Nomani
his paper presents our work on the annotation of intra-chunk dependencies on an English Treebank that was previously annotated with Inter-chunk dependencies. Given a natural language sentence, Intra-chunk dependencies show a relationship between words in a chunk and Inter-chunk dependencies showcase relationship among the chunks. For e.g. “James cut an apple with a knife”; In this sentence, chunking will result in (James) (cut) (an apple) (with a knife). Inter-chunk dependencies show dependency relation among these 4 chunks and Intra-chunk dependency show dependency relation between the words present in each chunk.
This exercise provides fully parsed dependency trees for the English treebank. We also report an analysis of the inter-annotator agreement for this chunk expansion task. Further, these fully expanded parallel Hindi and English treebanks were word aligned and an analysis for the task has been given. Issues related to intra-chunk expansion and alignment for the language pair Hindi-English are discussed and guidelines for these tasks have been prepared and released.
Semantic Rules Representation in Controlled Natural Language in FluentEditorCognitum
Abstract. The purpose of this paper is to present a way of representation of semantic rules (SWRL) in controlled natural language (English) in order to facilitate understanding the rules by humans interacting with a machine. The rule representation is implemented in FluentEditor – ontology editor with controlled natural language (CNL). The representation can be used in a lot of domains where people interact with machines and use specialized interfaces to define knowledge in a system (semantic knowledge base), e.g. representing medical knowledge and guidelines, procedures in crisis management or in management of any coordination processes. Such knowledge bases are able to support decision making in any discipline provided there is a knowledge stored in a proper semantic way.
MOLTO poster for ACL 2010, Uppsala SwedenOlga Caprotti
MOLTO is funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement FP7-ICT-247914. MOLTO's goal is to develop a set of tools for translating texts between multiple languages in real time with high quality. Languages are separate modules in the tool and can be varied; prototypes covering a majority of the EU's 23 official languages will be built.
http://molto-project.eu
Gellish A Standard Data And Knowledge Representation Language And OntologyAndries_vanRenssen
Database structures should allow for the expression of any fact that can be expressed in natural languages. This means that they should allow for any expression of facts in a universal formal language. Constraints should be specified in a separate layer. This article describes the basic concepts of such a universal semantic database structure and associated formal subset of a natural language.
This article advocates that information storage requirements should not be expressed in the form of data models or conceptual schemas, but database structures should allow for any expression in a general purpose language, whereas implementation constraints should be expressed as constraints on the use of the general purpose language.
Learning to Pronounce as Measuring Cross Lingual Joint Orthography Phonology ...gerogepatton
Machine learning models allow us to compare languages by showing how hard a task in each language might be to learn and perform well on. Following this line of investigation, we explore what makes a language “hard to pronounce” by modelling the task of grapheme-to-phoneme (g2p) transliteration. By training a character-level transformer model on this task across 22 languages and measuring the model’s proficiency against its grapheme and phoneme inventories, we show that certain characteristics emerge that separate easier and harder languages with respect to learning to pronounce. Namely the complexity of a language's pronunciation from its orthography is due to the expressive or simplicity of its grapheme-to phoneme mapping. Further discussion illustrates how future studies hould consider relative data sparsity per language to design fairer cross-lingual comparison tasks.
Learning to Pronounce as Measuring Cross Lingual Joint Orthography Phonology ...IJITE
Machine learning models allow us to compare languages by showing how hard a task in each language might be to learn and perform well on. Following this line of investigation, we explore what makes a language “hard to pronounce” by modelling the task of grapheme-to-phoneme (g2p) transliteration. By training a character-level transformer model on this task across 22 languages and measuring the model’s proficiency against its grapheme and phoneme
inventories, we show that certain characteristics emerge that separate easier and harder
languages with respect to learning to pronounce. Namely the complexity of a language's
pronunciation from its orthography is due to the expressive or simplicity of its grapheme-tophoneme mapping. Further discussion illustrates how future studies should consider relative data sparsity per language to design fairer cross-lingual comparison tasks.
Learning to Pronounce as Measuring Cross Lingual Joint Orthography Phonology ...ijrap
Machine learning models allow us to compare languages by showing how hard a task in each language might be to learn and perform well on. Following this line of investigation, we explore what makes a language “hard to pronounce” by modelling the task of grapheme-to-phoneme (g2p) transliteration. By training a character-level transformer model on this task across 22 languages and measuring the model’s proficiency against its grapheme and phoneme inventories, we show that certain characteristics emerge that separate easier and harder languages with respect to learning to pronounce. Namely the complexity of a language's pronunciation from its orthography is due to the expressive or simplicity of its grapheme-to phoneme mapping. Further discussion illustrates how future studies should consider relative data sparsity per language to design fairer cross-lingual comparison tasks.
LIT (Lexicon of the Italian Television) is a project conceived by the Accademia della Crusca, the leading research institution on the Italian language, in collaboration with CLIEO (Center for theoretical and historical Linguistics: Italian, European and Oriental languages), with the aim of studying frequencies of the Italian lexicon used in television content and targets the specific sector of web applications for linguistic research. The corpus of transcriptions is constituted approximately by 170 hours of random television recordings transmitted by the national broadcaster RAI (Italian Radio Television) during the year 2006.
Smart grammar a dynamic spoken language understanding grammar for inflective ...ijnlc
Spoken language understanding (SLU) is a key requirement of spoken dialogue systems (SDS). The role of
SLU parser is to robustly interpret the meanings of users’ utterance using a hand-crafted grammar that is
expensive to build. This task becomes even harder when the developer is creating a SLU grammar for
inflectional languages due to the different conjugations and declensions. This causes long grammar
definition files that are hard to structure and also to manage. In this paper, we propose a new and
alternative method, called Smart Grammar to facilitate the development of speech enabled applications.
This uses a morphological analyzer, in addition to the semantic parser, in order to convert each user
utterance in the canonical form.
Similar to Universal dependencies for Finnish (20)
(Paper Seminar detailed version) BART: Denoising Sequence-to-Sequence Pre-tra...hyunyoung Lee
(Detailed version) Paper seminar in NLP lab on "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"(2021.03.04)
(Paper Seminar short version) BART: Denoising Sequence-to-Sequence Pre-traini...hyunyoung Lee
(Short version) Paper seminar in NLP lab on "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"(2021.03.04)
Paper seminar of Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs in 2019 fall semester in Advanced Information Security class(2019.10.24).
Word embedding method of sms messages for spam message filteringhyunyoung Lee
This is presentation of 2019 the 6th IEEE International Conference on Big data and Smart Computing(ASC(the 3rd International Workshop on Affective and Sentimental Computing) of IEEE BigComp 2019), Feb. 2019. (2019. 02. 27)
Natural language processing open seminar For Tensorflow usagehyunyoung Lee
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
large-scale and language-oblivious code authorship identificationhyunyoung Lee
Paper seminar of Large-Scale and Language-Oblivious Code Authorship Identification in 2018 2 semester in Advanced Topics in Computer Science class(2018.11.06).
This is presentation to inform how to use NLTK(Natural Language Processing Toolkit) with NLTK book's simple examples in Information Retrieval and Data mining class as TA(2017.11.28).
This is presentation about what skip-gram and CBOW is in seminar of Natural Language Processing Labs.
- how to make vector of words using skip-gram & CBOW.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
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Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
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Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
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Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
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In this session we will cover:
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By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
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Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
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3. About The paper
There has been substantial recent interest in annotation schemes that can be applied consistently to
many languages
- The Universal Dependencies (UD) project seeks to introduce a cross-linguistically applicable part-
of-speech tagset, feature inventory, and set of dependency relations as well as a large number of
uniformly annotated treebank
The paper(2015) said universal dependency for Finnish is one of ten language in recent first releas
of UD project treebank data.
4. This paper details the mapping of previously introduce annotation to the UD standard
This paper additionally presents parsing experiments.
- comparing the performance of a state-of-the-art parser trained on a language specific annotation schema to
performance on the corresponding UD annotation.
- Tool and resources mentioned in this paper are available under open licenses from http://bionlp.utu.fi/ud-finnish.html
Conversion From Turku Dependence Treebank to Universal Dependencies
What is the treebank(Wikipedia)?
- in linguistics, a treebank is a parsed text corpus that annotates syntactic and semantic sentences structure.
Most syntactic treebank
annotate variants of either
phrase structure(left) or
dependency structure(right)
5. Universal dependency builds on the Google universal part-of-speech(POS) tagset, The interest interlingua of
morphosyntactic features, and Stanford Dependency
Universal dependency defines also a treebank storage format, CoNLL-U.
In this paper, they presents the adaptation of the UD guidelines to Finnish and the creation of the UD Finnish
treebank by conversion of the previously introduced Turku Dependency Treebank(TDT).
They present the comparison of parsing score between language-specific treebank annotation to that of UD
annotation
Most syntactic treebank
annotate variants of either
phrase structure(left) or
dependency structure(right)
6. The initial stage of the work is identifying similarities and difference between the TDT and UD
annotation guidelines, adapting the general UD guidelines and planning the implementation of the
conversion .
- Technically, the conversion is on pipeline of processing components, each of which consumed and produced
CoNLL-U formatted data.
In this paper, TDT is the source for the conversion. It consists of 15,000 sentences(200,000 words)
draw from a variety of sources and annotated in a Finnish-specific version of the Stanford
Dependencies(SD) scheme.
They also addressed several instances.
- tokenization differed from UD specifications.
- corrected a small number of sentence-splitting errors.
- updated the lemmas to improve both treebank-internal consistency and conformance with the UD specification
They further introduced a fully manually annotated morphology layer, replacing the automatically
generated morphological annotation of the initial data.
7. The Universal specification defines 17 POS tags, and requires that all conforming treebank use only
these tags
- While no language-specific POS tags can thus be defined in the primary POS annotation, the CoNLL-U format
allows a secondary POS tag to be assigned to each word to preserve treebank-specific information.
In the case of conjunctions from subordinating conjunctions (CONJ and SCONJ in UD, respectively).
Just four TDT tags, marking pronouns, punctuation, symbols and verbs, required further information
to resolve correctly.
8. : TDT-style vs UD style for syntax and part-of-speech annotation for a Finnish sentence.
9. The Universal Dependencies specification defines a set of 17 widely attested Morphological features
such as Case, Person, Number, Voice and Mood.
- However, by contrast to the POS tag annotation, the specification allows conforming treebanks to
introduce language specific features that are not included in this universal inventory.
10. UD defines as set of 40 broadly applicable dependency relation, further allowing language-specific
subtypes of these to be defined to meet the needs of specifics resources.
- Unlike the fairly straightforward mapping from morphological annotation, the conversion from TDT dependency
annotation to UD often required not only relabeling types, abut also changes to the tree structure.
11. The UD syntactic annotation is based on the universal Stanford Dependencies(SD) scheme.
The total number of dependency relation types defined in UD Finnish is 43, consisting of 32 universal
relations and 11 language-specific sub-types.
In the original TDD annotation, 46 dependency types are used, with an additional 4 types to mark non-
tree structures used in the second annotation layer of TDT.
Null token for omitted element in sentence BUT In UD, it is not used. Instead In UD, remnant relation.
TDT-style(top) vs UD-style(bottom)
12. The figure below shows tokens statistics for the 10 languages for which treebanks were included in
the initial UD data release.
- with over 200,000 tokens, The UD Finnish treebank is in a mid-size cluster among the UD version 1 languages
together with German, English, and Italian.
13. As baseline, we consider the most recent Finnish dependency distribution parser trained and
evaluated on the original distribution of TDT.
- they said that the conversion to UD has had no major effect on the parsing accuracy.
14. Soft constraint approach is where tags given by marmot are preserved, and OMorFi is only used to
select the lemma.
Hard-pos constraint is where the predicted POS can be found among the analyses given by OMoriFi
15. They have presented Universal Dependencies(UD) for Finnish, detailing the application of general UD
guidelines to the annotation of parts-of-speech, morphological feature, and dependency relations in
Finish and introducing a conversion from the previously released TurKu Dependency Treebank corpus
into the UD Finnish treebank released in the first UD data release.