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.
13. Constantin Orasan (UoW) Natural Language Processing for TranslationRIILP
This document discusses how natural language processing (NLP) techniques can help improve machine translation (MT). It describes some of the linguistic challenges in MT, such as ambiguity at the lexical, syntactic, semantic and pragmatic levels. It then discusses how various NLP tasks, such as tokenization, word sense disambiguation, and handling of named entities could enhance MT systems. Several studies that have successfully integrated NLP techniques like word sense disambiguation into statistical machine translation systems are also summarized.
Lectura 3.5 word normalizationintwitter finitestate_transducersMatias Menendez
This document describes a system for normalizing Spanish tweets using finite-state transducers. The system consists of three main components: 1) An analyzer that tokenizes tweets and identifies standard words, 2) Transducers that generate candidate words for out-of-vocabulary tokens by implementing linguistic models of spelling variation, 3) A statistical language model that selects the most probable sequence of words. The transducers are based on weighted finite-state automata and implement models of texting features such as logograms, shortenings, and non-standard spellings. An evaluation shows the system is effective at normalizing Spanish tweets.
This document discusses the font used in PPT presentations. The font is called Typeland.com and is based on the Kangxi dictionary style. It is a simplified Chinese font designed for presentations.
Building Universal Dependency Treebanks in KoreanJinho Choi
This paper presents three new dependency treebanks for Korean created by converting existing corpora to follow Universal Dependencies guidelines version 2. The treebanks are: 1) A revised version of the Google Universal Dependency Treebank with corrections to align with UDv2. 2) The Penn Korean Treebank containing over 5,000 sentences of newswire converted from phrase structure trees to dependency trees. 3) The KAIST Treebank containing over 27,000 sentences from various domains converted from phrase structure trees to dependency trees. Statistics are provided on the treebanks including part-of-speech tags and dependency labels.
The document discusses parsing in compilers. It defines parsing as the second stage after lexical analysis, where a parser checks if the stream of tokens from the lexical analyzer is grammatically correct by generating a parse tree. The fundamental theory behind parsing is context-free grammar, which is used to define languages and check parsing. The document then discusses context-free grammars, parse trees, ambiguity, and provides examples of grammars for Boolean expressions to illustrate parsing concepts.
This document presents machine learning techniques to identify verb subcategorization frames from Czech language corpora. It compares three statistical techniques and shows they can discover new subcategorization frames and label dependents as arguments or adjuncts with 88% accuracy. It describes the task, relevant properties of Czech including free word order and case marking, and how the techniques are applied without assuming a predefined frame set.
An Extensible Multilingual Open Source LemmatizerCOMRADES project
This document summarizes an article that presents an open-source lemmatizer called GATE DictLemmatizer. The lemmatizer currently supports lemmatization for English, German, Italian, French, Dutch, and Spanish. It uses a combination of automatically generated lemma dictionaries from Wiktionary and the Helsinki Finite-State Transducer Technology (HFST). An evaluation shows it achieves similar or better results than the TreeTagger lemmatizer for languages with HFST support, and still provides satisfactory results for languages without HFST support. The lemmatizer and tools to generate dictionaries are made freely available as open source.
The document provides an overview of markup languages and XML. It discusses the structure of XML documents including elements, tags, attributes, entities and the XML declaration. It also covers XML validation using DTDs, character encoding using UTF-8 and UTF-16, and special attributes like xml:lang.
13. Constantin Orasan (UoW) Natural Language Processing for TranslationRIILP
This document discusses how natural language processing (NLP) techniques can help improve machine translation (MT). It describes some of the linguistic challenges in MT, such as ambiguity at the lexical, syntactic, semantic and pragmatic levels. It then discusses how various NLP tasks, such as tokenization, word sense disambiguation, and handling of named entities could enhance MT systems. Several studies that have successfully integrated NLP techniques like word sense disambiguation into statistical machine translation systems are also summarized.
Lectura 3.5 word normalizationintwitter finitestate_transducersMatias Menendez
This document describes a system for normalizing Spanish tweets using finite-state transducers. The system consists of three main components: 1) An analyzer that tokenizes tweets and identifies standard words, 2) Transducers that generate candidate words for out-of-vocabulary tokens by implementing linguistic models of spelling variation, 3) A statistical language model that selects the most probable sequence of words. The transducers are based on weighted finite-state automata and implement models of texting features such as logograms, shortenings, and non-standard spellings. An evaluation shows the system is effective at normalizing Spanish tweets.
This document discusses the font used in PPT presentations. The font is called Typeland.com and is based on the Kangxi dictionary style. It is a simplified Chinese font designed for presentations.
Building Universal Dependency Treebanks in KoreanJinho Choi
This paper presents three new dependency treebanks for Korean created by converting existing corpora to follow Universal Dependencies guidelines version 2. The treebanks are: 1) A revised version of the Google Universal Dependency Treebank with corrections to align with UDv2. 2) The Penn Korean Treebank containing over 5,000 sentences of newswire converted from phrase structure trees to dependency trees. 3) The KAIST Treebank containing over 27,000 sentences from various domains converted from phrase structure trees to dependency trees. Statistics are provided on the treebanks including part-of-speech tags and dependency labels.
The document discusses parsing in compilers. It defines parsing as the second stage after lexical analysis, where a parser checks if the stream of tokens from the lexical analyzer is grammatically correct by generating a parse tree. The fundamental theory behind parsing is context-free grammar, which is used to define languages and check parsing. The document then discusses context-free grammars, parse trees, ambiguity, and provides examples of grammars for Boolean expressions to illustrate parsing concepts.
This document presents machine learning techniques to identify verb subcategorization frames from Czech language corpora. It compares three statistical techniques and shows they can discover new subcategorization frames and label dependents as arguments or adjuncts with 88% accuracy. It describes the task, relevant properties of Czech including free word order and case marking, and how the techniques are applied without assuming a predefined frame set.
An Extensible Multilingual Open Source LemmatizerCOMRADES project
This document summarizes an article that presents an open-source lemmatizer called GATE DictLemmatizer. The lemmatizer currently supports lemmatization for English, German, Italian, French, Dutch, and Spanish. It uses a combination of automatically generated lemma dictionaries from Wiktionary and the Helsinki Finite-State Transducer Technology (HFST). An evaluation shows it achieves similar or better results than the TreeTagger lemmatizer for languages with HFST support, and still provides satisfactory results for languages without HFST support. The lemmatizer and tools to generate dictionaries are made freely available as open source.
The document provides an overview of markup languages and XML. It discusses the structure of XML documents including elements, tags, attributes, entities and the XML declaration. It also covers XML validation using DTDs, character encoding using UTF-8 and UTF-16, and special attributes like xml:lang.
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.
Compiling Apertium Dictionaries with HFSTGuy De Pauw
This document discusses compiling Apertium dictionaries with HFST to leverage generalised compilation formulas and get more applications from fewer language descriptions. Compiling Apertium dictionaries natively in HFST provides benefits like uniform compilation across tools, improved resulting automata using HFST algorithms, and integrated complex finite-state morphology features. Additional applications like spellcheckers can also be automatically generated from the dictionaries.
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.
This document provides guidance on using a general-to-specific structure in academic writing. It begins by listing common reasons to use a G-S structure, such as in an exam answer, assignment introduction, or background paragraph. It then discusses how a G-S structure typically begins with a definition, statement of fact, or contrast. The document provides examples of short, sentence-level, and extended definitions. It also covers restrictive relative clauses and competing definitions. The overall purpose is to outline how to structure information from general to specific in academic writing.
SGML is a standard for specifying markup languages. It describes how to define document structure separately from presentation. XML is a simplified version of SGML used to store and transport data. Key differences between XML and HTML include XML focusing on data rather than presentation, being case sensitive, requiring closing tags, and preserving whitespace.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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The document discusses multimedia architecture and markup languages. It describes two common multimedia architectures - monolithic and shell architectures. It then covers markup languages, including HTML, XML, and SGML. XML is presented as being based on SGML but simplified for use on the web. Stylesheets are described as separating document contents from style to allow flexibility.
The document discusses text mining, including defining it as the extraction of information from unstructured text using computational methods. It covers topics such as structured vs unstructured data, common text mining practice areas like information retrieval and document clustering, and challenges in text mining including ambiguity in language. Pre-processing techniques for text mining are also outlined, such as normalization, tokenization, stemming and removing stop words to clean and prepare text for analysis.
8. Qun Liu (DCU) Hybrid Solutions for TranslationRIILP
The document provides an overview of hybrid machine translation approaches. It discusses selective machine translation which selects the best translation from multiple systems. Pipelined machine translation uses one system for pre-processing or post-processing of another system. Statistical post-editing uses statistical machine translation as a post-editor for rule-based machine translation outputs to improve the translation quality.
This document discusses the Interpreter and Iterator patterns from the Gang of Four (GoF) patterns. [1] The Interpreter pattern represents grammars and expressions as object structures to define a language and interpret sentences in that language. [2] The Iterator pattern allows iterating over elements of an object sequentially without exposing its underlying representation. [3] Both patterns have limitations and the document discusses when and how they could be improved.
Normalization is an alternative database design tool to data modeling that applies a series of rules to gradually improve the design. It involves identifying attributes that determine other attributes through functional dependencies and dividing tables to eliminate non-key attributes that are not functionally dependent on the primary key. The goal is to satisfy increasingly restrictive normal forms like 1NF, 2NF, 3NF and BCNF to reduce data redundancy and avoid undesirable characteristics like insertion, update and deletion anomalies.
This document provides an introduction to XML. It discusses that XML stands for Extensible Markup Language and is a text-based markup language used to store and transport data. It also describes that XML documents have a .xml file extension and reference a DTD or schema that defines the document structure. The document then gives examples of XML tags, elements, and attributes to illustrate XML syntax and building blocks.
This document discusses abstract and concrete syntaxes in computing languages and how GRDDL (Gleaning Resource Descriptions from Dialects of Languages) addresses some issues. It provides examples of different concrete syntaxes for arithmetic and RDF and explains that GRDDL allows authors to express RDF using XML without having to learn a specific RDF syntax or vocabulary by defining mappings from XML to RDF's abstract syntax. The document outlines some open questions about how GRDDL will handle transformation pipelines, URI resolution, validation, and its base rule.
This document is a preface to the second edition of "The Z Notation: A Reference Manual" by J.M. Spivey. It explains the motivation for revising and updating the reference manual, including addressing omitted language constructs, expanding the mathematical notation library, and improving explanations. The preface acknowledges contributions from others that helped improve the new edition. It also provides instructions for obtaining supplemental materials to support understanding and using the Z specification language.
This document provides an overview of a tutorial on statistical machine translation given by Dr. Khalil Sima'an. The tutorial is divided into two parts, with Part I covering data and models, including word-based models, alignment, symmetrization, and phrase-based models. Part II, given by Trevor Cohn, will cover decoding and efficiency. The tutorial will examine the statistical approach to machine translation using parallel corpora and will discuss generative source-channel frameworks and challenges in estimating translation probabilities from sparse data. It will also explore how current models induce structure in translation data using alignments between source and target language structures.
A Document Type Definition (DTD) defines the legal structure and elements of an XML document. It can be declared inline within an XML file or referenced externally. A DTD is useful for ensuring data validity, enabling data sharing through standardization, and verifying document structure. Elements are declared in a DTD using tags that specify allowed content and cardinality.
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.
ESR10 Joachim Daiber - EXPERT Summer School - Malaga 2015RIILP
The document discusses using syntactic preordering models to delimit the morphosyntactic search space for machine translation of morphologically rich languages. It explores preordering dependency trees of the source language to reduce word order variations and predicting morphological attributes on the source side to inform target language word selection. Experimental results show that non-local features and jointly learning which attributes to predict can improve translation performance over baselines. The work aims to combine preordering and morphology prediction to better exploit interactions between syntactic structure and inflectional properties.
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
This document summarizes the Monnet project, which aims to provide multilingual access to structured knowledge through an ontology-lexicon model called lemon. The project has several partners from both industry and research. Lemon represents lexical information relative to ontologies to support tasks like label translation and information extraction across languages. It draws on standards like the Lexical Markup Framework and relates ontologies to multilingual lexicons through a core path of lexicon, lexical entry, sense, reference, form, and representation. Future work includes applying these techniques to domains like financial reporting.
The Distributed Ontology Language (DOL): Use Cases, Syntax, and ExtensibilityChristoph Lange
The document discusses the Distributed Ontology Language (DOL) which aims to support semantic integration and interoperability across heterogeneous ontologies. DOL allows for logically heterogeneous ontologies, modular ontologies, and formal and informal links between ontologies. It has a formal semantics and can be serialized in XML, RDF, and text. Examples of applications that could benefit from DOL include an ontology repository engine and a multilingual map user interface driven by aligned ontologies.
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.
Compiling Apertium Dictionaries with HFSTGuy De Pauw
This document discusses compiling Apertium dictionaries with HFST to leverage generalised compilation formulas and get more applications from fewer language descriptions. Compiling Apertium dictionaries natively in HFST provides benefits like uniform compilation across tools, improved resulting automata using HFST algorithms, and integrated complex finite-state morphology features. Additional applications like spellcheckers can also be automatically generated from the dictionaries.
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.
This document provides guidance on using a general-to-specific structure in academic writing. It begins by listing common reasons to use a G-S structure, such as in an exam answer, assignment introduction, or background paragraph. It then discusses how a G-S structure typically begins with a definition, statement of fact, or contrast. The document provides examples of short, sentence-level, and extended definitions. It also covers restrictive relative clauses and competing definitions. The overall purpose is to outline how to structure information from general to specific in academic writing.
SGML is a standard for specifying markup languages. It describes how to define document structure separately from presentation. XML is a simplified version of SGML used to store and transport data. Key differences between XML and HTML include XML focusing on data rather than presentation, being case sensitive, requiring closing tags, and preserving whitespace.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The document discusses multimedia architecture and markup languages. It describes two common multimedia architectures - monolithic and shell architectures. It then covers markup languages, including HTML, XML, and SGML. XML is presented as being based on SGML but simplified for use on the web. Stylesheets are described as separating document contents from style to allow flexibility.
The document discusses text mining, including defining it as the extraction of information from unstructured text using computational methods. It covers topics such as structured vs unstructured data, common text mining practice areas like information retrieval and document clustering, and challenges in text mining including ambiguity in language. Pre-processing techniques for text mining are also outlined, such as normalization, tokenization, stemming and removing stop words to clean and prepare text for analysis.
8. Qun Liu (DCU) Hybrid Solutions for TranslationRIILP
The document provides an overview of hybrid machine translation approaches. It discusses selective machine translation which selects the best translation from multiple systems. Pipelined machine translation uses one system for pre-processing or post-processing of another system. Statistical post-editing uses statistical machine translation as a post-editor for rule-based machine translation outputs to improve the translation quality.
This document discusses the Interpreter and Iterator patterns from the Gang of Four (GoF) patterns. [1] The Interpreter pattern represents grammars and expressions as object structures to define a language and interpret sentences in that language. [2] The Iterator pattern allows iterating over elements of an object sequentially without exposing its underlying representation. [3] Both patterns have limitations and the document discusses when and how they could be improved.
Normalization is an alternative database design tool to data modeling that applies a series of rules to gradually improve the design. It involves identifying attributes that determine other attributes through functional dependencies and dividing tables to eliminate non-key attributes that are not functionally dependent on the primary key. The goal is to satisfy increasingly restrictive normal forms like 1NF, 2NF, 3NF and BCNF to reduce data redundancy and avoid undesirable characteristics like insertion, update and deletion anomalies.
This document provides an introduction to XML. It discusses that XML stands for Extensible Markup Language and is a text-based markup language used to store and transport data. It also describes that XML documents have a .xml file extension and reference a DTD or schema that defines the document structure. The document then gives examples of XML tags, elements, and attributes to illustrate XML syntax and building blocks.
This document discusses abstract and concrete syntaxes in computing languages and how GRDDL (Gleaning Resource Descriptions from Dialects of Languages) addresses some issues. It provides examples of different concrete syntaxes for arithmetic and RDF and explains that GRDDL allows authors to express RDF using XML without having to learn a specific RDF syntax or vocabulary by defining mappings from XML to RDF's abstract syntax. The document outlines some open questions about how GRDDL will handle transformation pipelines, URI resolution, validation, and its base rule.
This document is a preface to the second edition of "The Z Notation: A Reference Manual" by J.M. Spivey. It explains the motivation for revising and updating the reference manual, including addressing omitted language constructs, expanding the mathematical notation library, and improving explanations. The preface acknowledges contributions from others that helped improve the new edition. It also provides instructions for obtaining supplemental materials to support understanding and using the Z specification language.
This document provides an overview of a tutorial on statistical machine translation given by Dr. Khalil Sima'an. The tutorial is divided into two parts, with Part I covering data and models, including word-based models, alignment, symmetrization, and phrase-based models. Part II, given by Trevor Cohn, will cover decoding and efficiency. The tutorial will examine the statistical approach to machine translation using parallel corpora and will discuss generative source-channel frameworks and challenges in estimating translation probabilities from sparse data. It will also explore how current models induce structure in translation data using alignments between source and target language structures.
A Document Type Definition (DTD) defines the legal structure and elements of an XML document. It can be declared inline within an XML file or referenced externally. A DTD is useful for ensuring data validity, enabling data sharing through standardization, and verifying document structure. Elements are declared in a DTD using tags that specify allowed content and cardinality.
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.
ESR10 Joachim Daiber - EXPERT Summer School - Malaga 2015RIILP
The document discusses using syntactic preordering models to delimit the morphosyntactic search space for machine translation of morphologically rich languages. It explores preordering dependency trees of the source language to reduce word order variations and predicting morphological attributes on the source side to inform target language word selection. Experimental results show that non-local features and jointly learning which attributes to predict can improve translation performance over baselines. The work aims to combine preordering and morphology prediction to better exploit interactions between syntactic structure and inflectional properties.
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
This document summarizes the Monnet project, which aims to provide multilingual access to structured knowledge through an ontology-lexicon model called lemon. The project has several partners from both industry and research. Lemon represents lexical information relative to ontologies to support tasks like label translation and information extraction across languages. It draws on standards like the Lexical Markup Framework and relates ontologies to multilingual lexicons through a core path of lexicon, lexical entry, sense, reference, form, and representation. Future work includes applying these techniques to domains like financial reporting.
The Distributed Ontology Language (DOL): Use Cases, Syntax, and ExtensibilityChristoph Lange
The document discusses the Distributed Ontology Language (DOL) which aims to support semantic integration and interoperability across heterogeneous ontologies. DOL allows for logically heterogeneous ontologies, modular ontologies, and formal and informal links between ontologies. It has a formal semantics and can be serialized in XML, RDF, and text. Examples of applications that could benefit from DOL include an ontology repository engine and a multilingual map user interface driven by aligned ontologies.
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.
Ontology Integration and Interoperability (OntoIOp) – Part 1: The Distributed...Christoph Lange
The document introduces the Distributed Ontology Language (DOL), which is part of the Ontology Integration and Interoperability (OntoIOP) standard. DOL aims to enable logical and modular heterogeneity across ontologies to improve semantic integration and interoperability. It will serve as a logic-agnostic meta-language for structuring ontologies, ontology modules, and formal and informal links between ontologies. DOL is intended to have well-defined semantics and serializations to XML, RDF, and text to facilitate reuse of existing ontologies and reasoning over heterogeneous ontological representations.
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.
This document discusses methods for aligning word senses between languages using probabilistic sense distributions. It proposes two approaches: 1) Using only monolingual corpora and aligning senses based on similar sense distributions between closely related languages. 2) Leveraging parallel corpora to estimate sense distribution alignments and the most probable translation for each source sense. The approaches are tested on the Europarl corpus, first ignoring and then exploiting sentence alignments. Several examples are examined to validate the sense alignments. Key aspects include using word sense disambiguation to annotate corpora, estimating sense assignment distributions, and assigning translation weights between language pairs based on relative sense frequencies.
Ontology Integration and Interoperability (OntoIOp) – Part 1: The Distributed...Christoph Lange
The document discusses the Distributed Ontology Language (DOL), a proposed standard being developed by ISO for expressing heterogeneous ontologies and links between ontologies. DOL aims to achieve semantic integration and interoperability across knowledge representations. It will have a formal semantics and support multiple serialization formats. The standard is being developed to facilitate communication and reduce complexity for applications involving multiple ontologies.
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
Chapter 2 Text Operation and Term Weighting.pdfJemalNesre1
Zipf's law describes the frequency distribution of words in natural language corpora. It states that the frequency of any word is inversely proportional to its rank in the frequency table. Most words have low frequency, while a few words are used very frequently. Heap's law estimates how vocabulary size grows with corpus size, at a sub-linear rate. Text preprocessing techniques like stopword removal and stemming aim to reduce noise by excluding non-discriminative words from indexes.
This document discusses customizable segmentation of morphologically derived words in Chinese. It presents a system that can segment words in different ways to meet various user-defined standards. The system represents all morphologically derived words as word trees, where the root nodes are maximal words and leaf nodes are minimal words. Each non-terminal node has a resolution parameter that determines if its daughters are displayed as a single word or separate words. Different segmentations can then be obtained by specifying different combinations of these resolution parameters. This allows a single system to be customized for different segmentation needs.
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.
Natural Language Processing (NLP) involves developing computational techniques to analyze and understand human languages. Key techniques in NLP include sentiment analysis to classify emotions in text, text classification to categorize text, and tokenization to break text into discrete units like words. NLP is used to teach machines how to read and understand human language by identifying relationships between words and other linguistic elements.
This document describes a multilingual lexical simplification system for four Ibero-Romance languages: Spanish, Portuguese, Catalan, and Galician. The system uses a modular hybrid linguistic-statistical architecture that is the same across languages, with language-specific resources. It identifies complex words, performs word sense disambiguation, ranks synonyms, and inflects words using morphological generation. The system was evaluated on its ability to produce adequate and simple simplifications according to native speaker assessments.
Learning to Pronounce as Measuring Cross Lingual Joint Orthography Phonology ...gerogepatton
This document describes a study that used machine learning models to measure the complexity of pronouncing different languages. The study trained a character-level transformer model on a grapheme-to-phoneme transliteration task for 22 languages. It found that languages with a more direct grapheme-to-phoneme mapping, like Esperanto and Malay, were easier for the model to learn than languages with a more complex mapping, like Cantonese and Japanese. The complexity of a language's pronunciation was found to correlate with how direct or simple its grapheme-to-phoneme mapping is. The study also noted that comparing languages fairly requires considering differences in available training data per language.
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
1. The document proposes SmartGrammar, a new method for developing spoken language understanding grammars for inflectional languages like Italian.
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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.