This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
Introduction to Natural Language Processingrohitnayak
Natural Language Processing has matured a lot recently. With the availability of great open source tools complementing the needs of the Semantic Web we believe this field should be on the radar of all software engineering professionals.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
Introduction to Natural Language Processingrohitnayak
Natural Language Processing has matured a lot recently. With the availability of great open source tools complementing the needs of the Semantic Web we believe this field should be on the radar of all software engineering professionals.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
This is the presentation on Syntactic Analysis in NLP.It includes topics like Introduction to parsing, Basic parsing strategies, Top-down parsing, Bottom-up
parsing, Dynamic programming – CYK parser, Issues in basic parsing methods, Earley algorithm, Parsing
using Probabilistic Context Free Grammars.
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
NLP techniques used for Spell checking to recommend find error in the written word and also suggest a relevant word.
Algorithm: Jaccard Coefficient, The Levenshtein Distance
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
This is the presentation on Syntactic Analysis in NLP.It includes topics like Introduction to parsing, Basic parsing strategies, Top-down parsing, Bottom-up
parsing, Dynamic programming – CYK parser, Issues in basic parsing methods, Earley algorithm, Parsing
using Probabilistic Context Free Grammars.
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
NLP techniques used for Spell checking to recommend find error in the written word and also suggest a relevant word.
Algorithm: Jaccard Coefficient, The Levenshtein Distance
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
The recognition of spoken word can be viewed as classifying an auditory stimulus to one ‘’word form’’ category, chosen from many alternatives.
This process requires matching of the spoken input with the mental representation associated with the word candidates and selecting one among the several candidates that are atleast partially consistent with the input.
Process of recognizing a spoken word is that it starts from a string of phonemes (Dahan, Magnuson, 2006) establishes how these phonemes should be grouped to form words and passes these words into the next level of processing.
Some theories, though, take a broader view and blur the distinction between speech perception, spoken word recognition, and sentence processing (Elman, 2004; Gaskell & Marslen 1997; Klatt, 1979; McClelland, 1989).
Presentation of "Challenges in transfer learning in NLP" from Madrid Natural Language Processing Meetup Event, May, 2019.
https://www.meetup.com/es-ES/Madrid-Natural-Language-Processing-meetup/
Practical related work in repository: https://github.com/laraolmos/madrid-nlp-meetup
A SURVEY OF GRAMMAR CHECKERS FOR NATURAL LANGUAGEScsandit
ABSTRACT
Natural Language processing is an interdisciplinary branch of linguistic and computer science studied under the Artificial Intelligence (AI) that gave birth to an allied area called
‘Computational Linguistic’ which focuses on processing of natural languages on computational devices. A natural language consists of a large number of sentences which are linguistic units involving one or more words linked together in accordance with a set of predefined rules called grammar. Grammar checking is the task of validating sentences syntactically and is a prominent tool within language engineering. Our review draws on the recent development of various grammar checkers to look at past, present and the future in a new light. Our review covers grammar checkers of many languages with the aim of seeking their approaches, methodologies for developing new tool and system as a whole. The survey concludes with the discussion of various features included in existing grammar checkers of foreign languages as well as a few Indian Languages.
Automatic classification of bengali sentences based on sense definitions pres...ijctcm
Based on the sense definition of words available in the Bengali WordNet, an attempt is made to classify the
Bengali sentences automatically into different groups in accordance with their underlying senses. The input
sentences are collected from 50 different categories of the Bengali text corpus developed in the TDIL
project of the Govt. of India, while information about the different senses of particular ambiguous lexical
item is collected from Bengali WordNet. In an experimental basis we have used Naive Bayes probabilistic
model as a useful classifier of sentences. We have applied the algorithm over 1747 sentences that contain a
particular Bengali lexical item which, because of its ambiguous nature, is able to trigger different senses
that render sentences in different meanings. In our experiment we have achieved around 84% accurate
result on the sense classification over the total input sentences. We have analyzed those residual sentences
that did not comply with our experiment and did affect the results to note that in many cases, wrong
syntactic structures and less semantic information are the main hurdles in semantic classification of
sentences. The applicational relevance of this study is attested in automatic text classification, machine
learning, information extraction, and word sense disambiguation
International Journal of Engineering and Science Invention (IJESI) inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Imran Sarwar Bajwa, M. Abbas Choudhary [2006], "A Rule Based System for Speech Language Context Understanding", International Journal of Donghua University (English Edition), Jun 2006, Vol. 23 No. 06, pp:39-42
Crafting Your Customized Legal Mastery: A Guide to Building Your Private LLMChristopherTHyatt
Create a tailored legal education with a private LLM. Identify your specialization, research courses from reputable institutions, and leverage online platforms for flexibility. Craft a unique curriculum combining law with interdisciplinary studies, enhancing your expertise. Network with professionals, balance theory with practical experience, and stay updated on legal trends. Build a personalized learning journey to unlock your full potential in the legal landscape.
Domain Specific Terminology Extraction (ICICT 2006)IT Industry
Imran Sarwar Bajwa, M. Imran Siddique, M. Abbas Choudhary, [2006], "Automatic Domain Specific Terminology Extraction using a Decision Support System", in IEEE 4th International Conference on Information and Communication Technology (ICICT 2006), Cairo, Egypt. pp:651-659
P05- DINA: A Multi-Dialect Dataset for Arabic Emotion Analysis iwan_rg
By:
Muhammad Abdul-Mageed, Hassan Alhuzali, Dua'a Abu-Elhij'a and Mona Diab
Abstract
Although there has been a surge of research on sentiment analysis, less work has been done on the related task of emotion detection. Especially for the Arabic language, there is no literature that we know of for the computational treatment of emotion. This situation is due partially to lack of labelled data, a bottleneck that we seek to ease. In this work, we report efforts to acquire and annotate a multi-dialect dataset for Arabic emotion analysis.
P03- MANDIAC: A Web-based Annotation System For Manual Arabic Diacritization iwan_rg
By:
Ossama Obeid, Houda Bouamor, Wajdi Zaghouani, Mahmoud Ghoneim, Abdelati Hawwari, Mona Diab and Kemal Oflazer
Abstract
In this paper, we introduce MANDIAC, a web-based annotation system designed for rapid manual diacritization of Standard Arabic text. To expedite the annotation process, the system provides annotators with a choice of automatically generated diacritization possibilities for each word. Our framework provides intuitive interfaces for annotating text and managing the diacritization annotation process. In this paper we describe the annotation and the administration interfaces as well as the back-end engine. Finally, we demonstrate that our system doubles the annotation speed compared to using a regular text editor.
P04- Toward an Arabic Punctuated Corpus: Annotation Guidelines and Evaluation iwan_rg
By:
Wajdi Zaghouani and Dana Awad
Abstract
We present our effort to build a large scale punctuated corpus for Arabic. We illustrate in details our punctuation annotation guidelines designed to improve the annotation work flow and the inter-annotator agreement. We summarize the guidelines created, discuss the annotation framework and show the Arabic punctuation peculiarities. Our guidelines were used by trained annotators and regular inter-annotator agreement measures were performed to ensure the annotation quality. We highlight the main difficulties related to the Arabic punctuation annotation that arose during this project.
P02- Towards a New Arabic Corpus of Dyslexic Textsiwan_rg
By:
Maha Alamri and William John Teahan
Abstract
This paper presents a detailed account of the preliminary work for the creation of a new Arabic corpus of dyslexic text. The analysis of errors found in the corpus revealed that there are four types of spelling errors made as a result of dyslexia in addition to four common spelling errors. The subsequent aim was to develop a spellchecker capable of automatically correcting the spelling mistakes of dyslexic writers in Arabic texts using statistical techniques. The purpose was to provide a tool to assist Arabic dyslexic writers. Some initial success was achieved in the automatic correction of dyslexic errors in Arabic text.
P01- Toward a rich Arabic Speech Parallel Corpus for Algerian sub-Dialects iwan_rg
By:
Soumia Bougrine, Hadda Cherroun, Djelloul Ziadi, Abdallah Lakhdari and Aicha Chorana
Abstract
Speech datasets and corpora are crucial for both developing and evaluating accurate Natural Language Processing systems. While Modern Standard Arabic has received more attention, dialects are drastically underestimated, even they are the most used in our daily life and the social media, recently. In this paper, we present the methodology of building an Arabic Speech Corpus for Algerian dialects, and the preliminary version of that dataset of dialectal Arabic speeches uttered by Algerian native speakers selected from different Algeria’s departments. In fact, by means of a direct recording way, we have taken into account numerous aspects that foster the richness of the corpus and that provide a representation of phonetic, prosodic and orthographic varieties of Algerian dialects. Among these considerations, we have designed a rich speech topics and content. The annotations provided are some useful information related to the speakers, time-aligned orthographic word transcription. Many potential uses can be considered such as speaker/dialect identification and computational linguistic for Algerian sub-dialects. In its preliminary version, our corpus encompasses 17 sub-dialects with 109 speakers and more than 6 K utterances.
Keynote - Computational Processing of Arabic Dialects: Challenges, Advances a...iwan_rg
By:
Nizar Habash
Abstract
The Arabic language consists of a number of variants among which Modern Standard Arabic (MSA) has a special status as the formal, mostly written, standard of the media, culture and education across the Arab World. The other variants are informal, mostly spoken, dialects that are the languages of communication of daily life. Most of the natural language processing resources and research in Arabic have focused on MSA. However, recently, more and more research is targeting Arabic dialects. In this talk, we present the main challenges of processing Arabic dialects, and discuss common solution paradigms, current advances, and future directions.
رغم أن وجود المدونات اللغوية والأدوات الحاسوبية التي تسهل استخدامها في الدراسة اللغوية ليس أمرا جديدا، إلا أن الجهود العربية الخالصة التي تمت بخصوص بناء المدونات وأدوات معالجتها لازالت في بداياتها. والهدف من هذه المحاضرة هو تقديم لمحة عامة عن هذا الموضوع، ويمكن تلخيصها في ثلاثة محاور رئيسية. المحور الأول يقدم استعراضا موجزا لمعايير تصميم المدونات بحيث تكون متوازنة وممثلة للغرض الذي أنشئت من أجله، بالإضافة إلى المعلومات الأساسية التي يجب أن تتوفر بصورة واضحة عن نصوصها. أما المحور الثاني فيتعلق بتصميم وبناء المدونة اللغوية العربية لمدينة الملك عبدالعزيز للعلوم والتقنية (المدونة العربية)، والسمات التي تميزها عن غيرها من المدونات العربية الموجودة حتى الآن، مع استعراض سريع لأدوات الموقع المتوفرة حاليا، وتلك التي ستتوفر في الموقع الجديد بحول الله. أما المحور الثالث والأخير فيتعلق ببعض البرامج والأدوات التي طورت بالكامل في مدينة الملك عبدالعزيز للعلوم والتقنية أوتم تسهيل عملية استخدامها لغير المختصين لتكون منظومة كاملة قدر الإمكان لمعالجة المدونات اللغوية العربية حسب حاجة المستخدم مع التركيز بشكل رئيس على أهم هذه البرامج وهو نظام " غواص".
د.سلطان بن ناصر بن عبد الله المجيول، دكتوراه (لسانيات المدونات الحاسوبية وعلم اللغة التطبيقي، جامعة إكسيتر، بريطانيا، 1434هـ)، وماجستير (اللغة والنحو، تخصص: علم اللغة الاجتماعي والمصطلحات، جامعة الملك سعود، 1427هـ)، ودبلوم عالي (علم اللغة التطبيقي، جامعة الملك سعود، 1426هـ) وبكالوريوس (اللغة العربية، جامعة الملك سعود، 1424هـ).
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
2. OVERVIEW
Multilingual Natural Language
processing application: From theory to
practice
Edited by Daniel M. Bikel Imed Zitouni
IBM Press @ 2012
Two Parts:
I. Theory: 7 chapters
II. Practice: 9 chapters
10/30/2017 MASHAEL ALDUWAIS 2
3. ABOUT THE AUTHORS
Daniel M. Bikel
Current Position: Research Scientist @ Google
Previous: LinkedIn, Google, IBM
Education: Harvard University, University of
Pennsylvania
Interest: Syntax/parsing, information extraction,
multilingual systems, NLP systems design,
machine learning toolkits, language modeling.
Imed Zitouni
Current Position: Principle Researcher@ Microsoft
Previous: IBM, Bell-Labs, DIALOCA
Education: Université Henri Poincaré, Nancy
Interest: natural language processing, language
modeling, spoken dialog systems, speech
recognition, and machine learning.
10/30/2017 MASHAEL ALDUWAIS 3
4. BOOK CONTENT
Part I: Theory
Chapter 1 Finding the Structure of Words
Chapter 2 Finding the Structure of Documents
Chapter 3 Syntax
Chapter 4 Semantic Parsing
Chapter 5 Language Modeling
Chapter 6 Recognizing Textual Entailment
Chapter 7 Multilingual Sentiment and
Subjectivity Analysis
Part II: Practice
Chapter 8 Entity Detection and Tracking
Chapter 9 Relations and Events
Chapter 10 Machine Translation
Chapter 11 Multilingual Information Retrieval
Chapter 12 Multilingual Automatic
Summarization
Chapter 13 Question Answering
Chapter 14 Distillation
Chapter 15 Spoken Dialog Systems
Chapter 16 Combining Natural Language
Processing Engines
10/30/2017 MASHAEL ALDUWAIS 4
5. CHAPTER 1. FINDING THE STRUCTURE OF WORDS
الكلمات كيبتر
Morphological parsing: discovery of word structure
Tokens: words
In Arabic, certain tokens are concatenated in writing with the preceding or the following ones, possibly changing
their forms as well. (called clitics).
Lexemes: the concept behind a linguistic form and the set of alternative that can express it.
Lexical categories of verbs, nouns, adjectives, conjunctions, particles, or other parts of speech.
Turning singular into plural
Morphemes: structural components of word form (segments or morphs). Ex: dis-agree-ment-s
Typology: divides languages into groups by characterizing the prevalent morphological
phenomena in those languages. Ex: Isolating, Synthetic, Agglutinative, Fusional.
10/30/2017 MASHAEL ALDUWAIS 5
6. CHAPTER 1. FINDING THE STRUCTURE OF WORDS
الكلمات كيبتر
Issues and Challenges:
Irregularity: word forms that are not described by a prototypical linguistic model.
Ambiguity: word forms be understood in multiple ways out of the context of their discourse.
Productivity: Is the inventory of words in a language finite, or is it unlimited?
Morphological Models:
Dictionary Lookup
Finite-State Morphology
Unification-Based Morphology
Functional Morphology
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7. CHAPTER 2. FINDING THE STRUCTURE OF DOCUMENTS
النص كيبتر
Some (NLP) tasks use sentences as the basic processing unit:
Parsing, machine translation, automatic speech recognition (ASR) systems, and semantic role
labeling
Sentence boundary detection (sentence segmentation): Automatically segmenting a
sequence of word tokens into sentence units.
Topic segmentation (discourse or text segmentation): Automatically dividing a stream
of text or speech into topically homogeneous blocks.
A boundary classification problem:
Depending on the type of input (i.e., text versus speech), different features may be used.
Performance have improved by exploiting very high-dimensional feature sets.
10/30/2017 MASHAEL ALDUWAIS 7
8. CHAPTER 3. SYNTAX
النحو
Syntax Parsing: (syntax analysis): discover the various predicate-argument
dependencies that may exist in a sentence.
Parse natural language text to provide syntactic trees.
Recursively partition the words in the sentence into individual phrases such as verb or noun.
Used for text-to-speech, machine translation, summarization, and paraphrasing application.
10/30/2017 MASHAEL ALDUWAIS 8
9. CHAPTER 3. SYNTAX
النحو
Treebanks:
A collection of sentences where each sentence is provided a complete syntax analysis.
(Annotated text corpus)
The syntactic analysis for each sentence has been judged by a human expert.
A style book or set of annotation guidelines is typically written before the annotation process
to ensure a consistent scheme of annotation throughout the treebank.
Two main approaches to construct treebanks: dependency graphs and phrase structure.
Challenges:
Ambiguity. Chose from an exponentially large number of alternative analyses.
Language issues: tokenization, case, encoding, word segmentation and morphology.
10/30/2017 MASHAEL ALDUWAIS 9
10. CHAPTER 4. SEMANTIC PARSING
الدالل التحليل
Semantic parsing: identifying meaning chunks contained in an information signal in
an attempt to transform it into some data structure that can be manipulated by a
computer to perform higher level tasks.
Two types of representations:
Deep semantic parsing: taking natural language input and transforming it into a meaning
representation. Domain-dependent.
Problem: reusability of the representation across domains is very limited.
Shallow semantic parsing: deals with the four main aspects of language: structural ambiguity,
word sense, entity and event recognition, and predicate argument structure recognition.
General-purpose.
Problem: difficult to construct a general-purpose ontology.
10/30/2017 MASHAEL ALDUWAIS 10
11. CHAPTER 4. SEMANTIC PARSING
الدالل التحليل
A semantic theory should be able to:
1. Explain sentences having ambiguous meanings. For example, it should account for the
fact that the word bill in the sentence The bill is large is ambiguous in the sense that it
could represent money or the beak of a bird.
2. Resolve the ambiguities of words in context. For example, if the same sentence is
extended to form The bill is large but need not be paid, then the theory should be able
to disambiguate the monetary meaning of bill.
3. Identify meaningless but syntactically well-formed sentences, such as the famous
example by Chomsky: Colorless green ideas sleep furiously.
4. Identify syntactically or transformationally unrelated paraphrases of a concept
having the same semantic content.
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12. CHAPTER 4. SEMANTIC PARSING
الدالل التحليل
Semantic parsing can be considered as part of semantic interpretation.
Requirements for Semantic Interpretation:
Structural Ambiguity: transforming a sentence into its underlying syntactic representation.
Word Sense: the same word type is used in different contexts.
EX: She nailed the loose arm of the chair with a hammer. VS. She went to the beauty salon to get a manicure.
Entity and Event Resolution: named entity recognition and coreference resolution.
Predicate-Argument Structure: identifying the participants of the entities in these events.
Can be defined as the identification of who did what to whom, when, where, why, and how
Meaning Representation: build a semantic representation that can then be manipulated by
algorithms to various application ends (called deep representation). A domain-specific
approach.
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13. CHAPTER 5. LANGUAGE MODELING
نمذجةاللغة
A statistical model that assigns a probability to a sentence.
Specifies the a priori probability of a particular word sequence in the language of interest.
Given an alphabet or inventory of units Σ and a sequence W = w1w2 ...wt ∈ Σ∗, a language
model can be used to compute the probability of W based on parameters previously
estimated from a training set.
LM is usually combined in speech recognition, machine translation.
A standard tool in information retrieval, spell correction, summarization, authorship
identification, and document classification.
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14. CHAPTER 5. LANGUAGE MODELING
نمذجةاللغة
n-Gram Models: all previous words except for the (n − 1) words directly preceding
the current word are irrelevant for predicting the current word, or, alternatively, that
they are equivalent.
Evaluation criteria: coverage rate, perplexity.
Language Model Adaptation: designing and tuning a language model such that it
performs well on a new test set for which little equivalent training data is available.
Methods: Mixture language models, topic-dependent language model, trigger models.
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15. CHAPTER 5. LANGUAGE MODELING
نمذجةاللغة
Types of Language Models: other than n-gram language model
Class-Based Language Models
Variable-Length Language Models
Discriminative Language Models
Syntax-Based Language Models
MaxEnt Language Models
Factored Language Models
Bayesian Topic-Based Language Models
Neural Network Language Models
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16. CHAPTER 5. LANGUAGE MODELING
نمذجةاللغة
Language Modeling Problems:
Language-Specific Modeling Problems:
In Arabic, decomposition may be required. Integrating morphological information into the language
model is helpful for modeling dialectal Arabic.
Spoken versus Written Languages:
Many of the world’s 6,900 languages are spoken languages, that is, languages without a writing
system (dialects).
In this case: the only way of obtaining language model training data is to manually transcribe the
language or dialect. This is a costly and time-consuming process because it involves (i) the
development of a writing standard, (ii) training native speakers to use the writing system consistently
and accurately, and (iii) the actual transcription effort. In the second case, those text resources that
can be obtained for the language in question (e.g., from the web) will need to be normalized, which
can also be a laborious process
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17. CHAPTER 6. RECOGNIZING TEXTUAL ENTAILMENT
النص نالتضمي عىل التعرف
Textual entailment is defined as a directional relationship between pairs of text
expressions, denoted by T, the entailing text, and H, the entailed hypothesis. We say
that T entails H if the meaning of H can be inferred from the meaning of T, as would
typically be interpreted by people.
Applications of Textual Entailment Solutions:
Summarization.
Exhaustive Search for Relations
Question Answering
Machine Translation
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18. CHAPTER 7. MULTILINGUAL SENTIMENT AND
SUBJECTIVITY ANALYSIS
المتعدد للغات الذات والتحليل المشاعر تحليلة
Subjectivity classification: labels text as either subjective or objective.
Sentiment classification: classifies subjective text as either positive, negative, or
neutral.
Used in automatic expressive text-to-speech synthesis, tracking sentiment timelines in online
forums and news, and mining opinions from product reviews.
Tools: two main types of tools:
I. Rule-based systems: relying on manually or semi-automatically constructed lexicons. Ex:
OpinionFinder.
II. Machine learning classifiers: trained on opinion-annotated corpora. Ex: Wiebe, Bruce,
and O’Hara .
Corpora: subjectivity and sentiment annotated corpora used to train automatic
classifiers, and as resources to extract opinion mining lexicons.
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19. CHAPTER 7. MULTILINGUAL SENTIMENT AND
SUBJECTIVITY ANALYSIS
المتعدد للغات الذات والتحليل المشاعر تحليلة
Lexicons:
OpinionFinder: contains 6,856 unique entries, out of which 990 are multiword expressions.
Each entry is also associated with a polarity label, indicating whether the corresponding
word or phrase is positive, negative, or neutral.
General Inquirer: a dictionary of about 10,000 words grouped into about 180 categories,
which have been widely used for content analysis. It includes semantic classes (e.g., animate,
human), verb classes (e.g., negatives, becoming verbs), cognitive orientation classes (e.g.,
causal, knowing, perception), and others. Two of the largest categories in the General
Inquirer are the valence classes, which form a lexicon of 1,915 positive words and 2,291
negative words.
SentiWordNet: Built on top of WordNet, which assigns each synset in WordNet with a score
triplet (positive, negative, and objective), indicating the strength of each of these three
properties for the words in the synset.
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20. CHAPTER 7. MULTILINGUAL SENTIMENT AND
SUBJECTIVITY ANALYSIS
المتعدد للغات الذات والتحليل المشاعر تحليلة
Word- and Phrase-Level Annotations: three main directions:
i. manual annotations, which involve human judgment of selected words and phrases,
ii. automatic annotations based on knowledge sources such as dictionaries,
iii. automatic annotations based on information derived from corpora.
Sentence-Level Annotations: corpus annotations are often required either as an end goal for
various text-processing applications (e.g., mining opinions from the Web, classification of
reviews into positive and negative), or as an intermediate step toward building automatic
subjectivity and sentiment classifiers. Two methods:
i. dictionary-based, consisting of rule-based classifiers relying on lexicons,
ii. corpus-based, consisting of machine learning classifiers trained on preexisting annotated
data.
Document-Level Annotations: applications, such as review classification or web opinion
mining, often require corpus-level annotations of subjectivity and polarity.
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21. CHAPTER 8. ENTITY DETECTION AND TRACKING
ومتابعتها االعالم أسماء عىل التعرف
Mention detection:
Detecting the boundary of a mention and optionally identifying the semantic type (e.g.,
PERSON or ORGANIZATION) and other attributes (e.g., named, nominal, or pronominal).
Closed to named entity recognition.
Mentions: any instances of textual references to objects or abstractions, which can be either
named (e.g., John Mayor), nominal (e.g., the president), or pronominal (e.g., she, it).
Can be formulated as a classification problem by assigning a label to each token in the text.
Coreference resolution:
Clustering mentions referring to the same entity into equivalence classes.
Machine learning-based approaches: learn a model from training data that assigns a score
to a pair of mentions indicating the likelihood that the two mentions refer to the same entity.
Mentions are then clustered into entities on the basis of mention-pair scores.
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22. CHAPTER 9. RELATIONS AND EVENTS
واألحداث العالقات
Relation Extraction Systems: systems capable of finding semantic relations among entities.
Relation extraction can be considered as multiclass classification problem, with several classes of
features including structural, lexical, entity-based, syntactic, and semantic.
Relation Extraction Types:
Extracting relations typically associated with lexical ontologies, such as meronymy, hyponymy, and
troponymy;
Extracting relations similar in nature, such as detecting that verb1 expresses the same concept as
verb2 but in a stronger fashion; and
Finding similarity enablement, that is, detecting that the action expressed by verb1 is a
prerequisite for the action expressed by verb2.
Identifying general semantic links between potentially heterogeneous entities, such as employment
relations between people and companies, cause of death relations between diseases and
people, or ownership of one entity (such as a company) by another.
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23. CHAPTER 9. RELATIONS AND EVENTS
واألحداث العالقات
National Institute of Standards and Technology (NIST) ACE evaluations:
PHYS (physical): A spatial relation denoting that a person is located at or near a facility, or a
location.
PART-WHOLE: A spatial relation denoting that a facility, a location, or a gpe is a part of another
facility.
PER-SOC (personal-social): Personal-social relations capture links between people. Relations can
be business-related, can be family-based.
ORG-AFF (organization-affiliation): This type of relation pertains to connections between persons
and organizations. A person could be employed by an organization or could be a member.
GEN-AFF (general-affiliation): citizenship, residence in a country, religious affiliation, and
ethnicity.
ART (artifact): A relation between a user, inventor, or manufacturer and the artifact itself.
METONYMY: A relation between two different aspects of the same underlying entity.
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24. CHAPTER 9. RELATIONS AND EVENTS
واألحداث العالقات
Events: denotes any change of state in the world that is described using natural
language text.
Event extraction: is the use of any algorithm to extract a structured representation of
that change of state, crucially including the entities involved.
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25. CHAPTER 10. MACHINE TRANSLATION
اآللية جمةالت
converting text in one language into another while preserving its meaning.
Research started in the 1940s at IBM. Most profound change can be dated back to
1988.
Statistical Machine Translation:
Using large corpora of translated texts, typically many millions of words.
Learn the rules of translation from corpora and provide the basis for a decoding algorithm
that finds the best translation for a given input sentence
Machine translation is being integrated into various applications: crosslingual
information retrieval, speech translation, and tools for translators.
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26. CHAPTER 10. MACHINE TRANSLATION
اآللية جمةالت
Word Alignment: Learning translation rules from a parallel corpus.
Unsupervised learning problem.
A word-aligned parallel corpus allows the estimation of phrase-based and tree-based
models and other approaches.
Evaluation:
Human Assessment: ask human judges if the output constitutes a correct translation. Is it
fluent? Is the translation adequate?
Automatic Evaluation Metrics: evaluation campaigns for evaluation metrics, where different
metric developers compete for the highest correlation with human judges. Runs similarity
measures test between MT output and the reference translations. Count: matches, insertions,
deletions.
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27. CHAPTER 10. MACHINE TRANSLATION
اآللية جمةالت
Current Research:
The development of models that more closely mirror linguistic understanding of language,
The application of novel machine learning methods to the estimation problem of learning
Translation rules from the data, and
The attempts to exploit various types of data sources, which are often not in the desired
domain or may not be even proper sentence-by-sentence translations at all.
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28. CHAPTER 10. MACHINE TRANSLATION
اآللية جمةالت
Linguistic Challenges:
Lexical Choice: word sense disambiguation. n-gram language model, try to capture
effectively local context information that is very useful for making the right lexical choice.
Morphology: when translating into morphologically rich languages, it is often not clear from
the local context which morphological variant to choose.
Word Order: To define which of the entities mentioned in the sentence is the subject and
which are the objects and what their roles are, languages such as English use word order.
Future Directions:
The estimations of parameter values in MT models.
Syntactic models
Using comparable or purely monolingual data instead of parallel data.
Integrating statistical machine translation into other information processing applications.
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29. CHAPTER 11. MULTILINGUAL INFORMATION RETRIEVAL
اللغات متعدد المعلومات جاعاست
Importance:
Improvements in machine translation (MT), have fostered the development of effective multilingual
retrieval systems.
The growing number of non-English Internet users and non-English content on the Web.
Advent of Web 2.0 technologies.
Crosslingual information retrieval (CLIR):
Retrieving documents relevant to a given query in some language (query language) from a
collection of documents in some other language (collection language).
Approaches: Translation-Based Approaches, Inter-lingual Document Representations.
Multilingual information retrieval (MLIR):
Involves corpora containing documents written in different languages.
MLIR requires different index organization and relevance computation strategies than CLIR.
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30. CHAPTER 11. MULTILINGUAL INFORMATION RETRIEVAL
اللغات متعدد المعلومات جاعاست
Evaluation:
Metrics: Relevance Assessments, precision and recall.
Evaluation Campaigns: Text REtrieval Conference (TREC), Crosslingual Evaluation Forum
(CLEF), NII Test Collection for IR Systems (NTCIR), Forum for Information Retrieval Evaluation
(FIRE).
Parallel Corpora: JRC-Acquis, Multext Dataset, Canadian Hansards, Europarl.
Tools, Software, and Resources:
Preprocessing: Content Analysis Toolkit (Tika), Snowball Stemmer, HTML Parser, BananaSplit.
IR Frameworks: Lucene, Terrier and Lemur.
Evaluation: TREC eval.
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31. CHAPTER 12. MULTILINGUAL AUTOMATIC
SUMMARIZATION
اللغات متعدد اآلل التلخيص
In multilingual summarization, texts written in multiple languages are used by
summarization systems.
Types of summary:
An informative summary, is a compressed version of the original covering the most important
facts reported in the input text(s) (e.g., summary of a journal article).
An indicative summary covers topics in the input text without providing further details (e.g.,
keywords for scientific papers).
An evaluative summary gives an opinion on the input text most often by comparing it to
similar documents.
An elaborative summary can provide more details of parts of a large document or the
document linked to by the current document to help navigation through large documents or
linked collections such as Wikipedia.
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32. CHAPTER 12. MULTILINGUAL AUTOMATIC
SUMMARIZATION
اللغات متعدد اآلل التلخيص
Crosslingual summarization: spread out over multiple source languages, and the
resulting summary is presented in one (or more) target languages.
Requires the integration of multiple source documents coming from different languages
Named entities are often transcribed differently in different languages (coreference
resolution)
Languages encode number and gender agreement differently as English lacks grammatical
gender (Anaphora resolution).
Evaluation:
Extrinsic evaluations measure the usefulness of summaries by measuring how much they can
help in performing another information-processing task.
Intrinsic evaluations measure and reflect summary quality and can be used in various stages
in a summarization development cycle.
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33. CHAPTER 12. MULTILINGUAL
AUTOMATIC SUMMARIZATION
اللغات متعدد اآلل التلخيص
Summarization systems are divided into three stages:
1. For the analysis stage, summarization systems may
represent the text in the form of a graph. This may be a
linguistically motivated discourse tree or a matrix
representation based on sentence-to-sentence similarity.
2. The transformation process can be carried out via graph-
based algorithms such as PageRank or by machine
learning–based classifiers that learn to classify sentences
according to their relevancy.
3. Multilingual approaches have to face many language-
dependent challenges such as tokenization, anaphoric
expressions, and discourse structure for the realization of
the summary.
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34. CHAPTER 13. QUESTION ANSWERING
األسئلة عىل اإلجابة
QA: Retrieve answers to user questions from information sources.
Follows a pipeline layout consisting of components for
1. Transforming questions into search engine queries
2. Retrieving related text using existing IR systems,
3. Extracting and scoring candidate answers.
Questions are classified with regard to their expected answer,
factoid questions, which ask for concise answers such as named entities (e.g., What is the capital of Turkey?),
list questions seeking lists of such factoid answers (e.g., Which countries are in NATO?).
Attempts have been made to tackle questions with complex answers, such as definitional questions requesting
information on a given topic, including biographies for people (e.g., Who is Albert Einstein?),
relationship questions (e.g., What is the relationship between the Taliban and Al-Qaeda?),
opinion questions (e.g., What do people like about IKEA?).
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36. CHAPTER 13. QUESTION ANSWERING
األسئلة عىل اإلجابة
Future Directions:
Reliable confidence estimates for the top answers.
Crosslingual QA systems that translate answers back to the language in which the question
was asked.
General-purpose QA algorithms and techniques that can be adapted rapidly to new tasks
and achieve high performance across different domains.
QA systems that provide complex answers.
How and why questions seeking explanations or justifications
Yes–no questions requiring a system to determine whether the combined knowledge in the available information
sources entails a hypothesis.
Deeper NLP techniques to find answers in sources that lack semantic redundancy.
QA systems that support user interactions and information sources in different languages.
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37. CHAPTER 14. DISTILLATION
االستخالص
Distillation queries can be complex and require complex answers.
For example: Describe the reactions of <COUNTRY> to <EVENT>.
The Rosetta Consortium Distillation System: built as part of the GALE program. The system is
designed to answer distillation queries run against a large corpus composed of text documents and
audio recordings in multiple languages: English, Arabic, and Mandarin. Text sources are assumed to
belong to two main categories: structured and unstructured.
Three Stages:
Document preparation: recordings are transcribed, and text and transcripts in foreign languages are
translated into English. Tokenization, part-of-speech (POS) tagging, parsing, mention detection, and semantic
role labeling rely on maximum entropy (MaxEnt) models is performed.
Indexing: documents are indexed using an open source search engine, Lucene.
Query answering: takes as input a GALE-style query, and returns a list of main snippets with associated
supporting snippets and citations, sorted in decreasing order of relevance to the query. The architecture of
the system consists of five stages: query preprocessing, document retrieval, snippet filtering, snippet
processing, and planning.
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38. CHAPTER 14. DISTILLATION
االستخالص
Challenges
The lack of publicly available corpora for measuring the progress of the field a
The difficulty and cost of evaluating the outputs of distillation systems due to the lack of
automatic metrics.
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39. CHAPTER 15. SPOKEN DIALOG SYSTEMS
اآلل الحوار أنظمة
A spoken dialog system is a complex machine that
manages goal-oriented user interactions.
Functional architecture:
Speech recognition and understanding module: to
assign one or more semantic tags to each speech
input.
Speech generation module: Rule-based grammar is
used, which encodes both the syntax and semantics of
possible utterances.
Dialog manager: uses a finite-state machine approach
by explicitly encoding the whole interaction into what
is generally known as call-flow.
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40. CHAPTER 16. COMBINING NATURAL LANGUAGE
PROCESSING ENGINES
الطبيعية اللغة معالجة كاتمحر نبي الجمع
Many engines are now attaining accuracy sufficient to enable combining them to
serve more complex tasks than were possible before.
Example applications: semantic search, enterprise reporting and other business intelligence,
question answering, medical-abstract mining, and crosslingual search, audio/video search
and cataloging, speech-to-speech translation, and foreign broadcast news analysis.
Applications like these share many common engines, such as speaker identification, speech-
to-text, text tokenization, grammatical parsing, named entity detection, coreference analysis,
part-of-speech labeling, and translation.
Aggregation poses several challenges: Heterogeneous computing environments,
Remote operation, Data formats, Exception handling.
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41. CHAPTER 16. COMBINING NATURAL LANGUAGE
PROCESSING ENGINES
الطبيعية اللغة معالجة كاتمحر نبي الجمع
Desired Attributes of Architectures for Aggregating Speech and NLP Engines:
Flexible, Distributed Componentization.
Computational Efficiency.
Data-Manipulation Capabilities.
Robust Processing.
Frameworks that support integration into more complex applications:
UIMA
GATE: General Architecture for Text Engineering
InfoSphere Streams
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