Tomoyuki Kajiwara, Kazuhide Yamamoto. Evaluation Dataset and System for Japanese Lexical Simplification. In Proceedings of the ACL-IJCNLP 2015 Student Research Workshop, pp.35-40. Beijing, China, July 2015.
This document provides an introduction to natural language processing (NLP). It discusses the brief history of NLP, major NLP tasks such as machine translation and text classification, common NLP techniques like part-of-speech tagging and parsing, main problems in NLP including ambiguity, and an overview of the topics to be covered in the course such as tokenization, parsing, and topic modeling. The course aims to use Python and R to complete various NLP tasks.
Nonparametric Bayesian Word Discovery for Symbol Emergence in RoboticsTadahiro Taniguchi
This is a material for invited talk in the workshop on Machine Learning Methods for High-
Level Cognitive Capabilities in Robotics 2016 (ML-HLCR2016) held in IROS2016, Korea.
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models.
2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representations.
3. Recent models like word2vec use neural networks to learn word embeddings that capture linguistic regularities and can be used for tasks like analogy-making and machine translation.
Lecture 1: Semantic Analysis in Language TechnologyMarina Santini
This document provides an introduction to a course on semantic analysis in language technology taught at Uppsala University in Sweden. It outlines the course website, contact information for the instructor, intended learning outcomes, required readings, assignments and examination. The course focuses on applying semantic analysis methods in natural language processing tasks like sentiment analysis, information extraction, word sense disambiguation and predicate-argument extraction. It will introduce students to representing and modeling meaning in language through formal logics and semantic frameworks.
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Saurabh Kaushik
This document discusses natural language processing (NLP) and language modeling. It covers the basics of NLP including what NLP is, its common applications, and basic NLP processing steps like parsing. It also discusses word and sentence modeling in NLP, including word representations using techniques like bag-of-words, word embeddings, and language modeling approaches like n-grams, statistical modeling, and neural networks. The document focuses on introducing fundamental NLP concepts.
This document is a lecture on tokenization and word counts in natural language processing. It discusses concepts like types and tokens, Zipf's law and Heap's law which relate the number of word types to the number of tokens in a text. The document also covers challenges in tokenization like sentence segmentation and provides examples of rule-based and machine learning approaches to tokenization. It introduces word normalization techniques like lemmatization and stemming and provides exercises for students to practice word counting, lemmatization, stemming and removing stop words from texts.
This document provides an introduction to natural language processing (NLP). It discusses the brief history of NLP, major NLP tasks such as machine translation and text classification, common NLP techniques like part-of-speech tagging and parsing, main problems in NLP including ambiguity, and an overview of the topics to be covered in the course such as tokenization, parsing, and topic modeling. The course aims to use Python and R to complete various NLP tasks.
Nonparametric Bayesian Word Discovery for Symbol Emergence in RoboticsTadahiro Taniguchi
This is a material for invited talk in the workshop on Machine Learning Methods for High-
Level Cognitive Capabilities in Robotics 2016 (ML-HLCR2016) held in IROS2016, Korea.
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models.
2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representations.
3. Recent models like word2vec use neural networks to learn word embeddings that capture linguistic regularities and can be used for tasks like analogy-making and machine translation.
Lecture 1: Semantic Analysis in Language TechnologyMarina Santini
This document provides an introduction to a course on semantic analysis in language technology taught at Uppsala University in Sweden. It outlines the course website, contact information for the instructor, intended learning outcomes, required readings, assignments and examination. The course focuses on applying semantic analysis methods in natural language processing tasks like sentiment analysis, information extraction, word sense disambiguation and predicate-argument extraction. It will introduce students to representing and modeling meaning in language through formal logics and semantic frameworks.
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Saurabh Kaushik
This document discusses natural language processing (NLP) and language modeling. It covers the basics of NLP including what NLP is, its common applications, and basic NLP processing steps like parsing. It also discusses word and sentence modeling in NLP, including word representations using techniques like bag-of-words, word embeddings, and language modeling approaches like n-grams, statistical modeling, and neural networks. The document focuses on introducing fundamental NLP concepts.
This document is a lecture on tokenization and word counts in natural language processing. It discusses concepts like types and tokens, Zipf's law and Heap's law which relate the number of word types to the number of tokens in a text. The document also covers challenges in tokenization like sentence segmentation and provides examples of rule-based and machine learning approaches to tokenization. It introduces word normalization techniques like lemmatization and stemming and provides exercises for students to practice word counting, lemmatization, stemming and removing stop words from texts.
Engineering Intelligent NLP Applications Using Deep Learning – Part 2 Saurabh Kaushik
This document discusses how deep learning techniques can be applied to natural language processing tasks. It begins by explaining some of the limitations of traditional rule-based and machine learning approaches to NLP, such as the lack of semantic understanding and difficulty of feature engineering. Deep learning approaches can learn features automatically from large amounts of unlabeled text and better capture semantic and syntactic relationships between words. Recurrent neural networks are well-suited for NLP because they can model sequential data like text, and convolutional neural networks can learn hierarchical patterns in text.
This document provides an overview of sentiment analysis and discusses why it is an important area of research in language technology. Sentiment analysis involves detecting positive or negative opinions in text about products, politicians, or other topics. It has many applications, such as determining how consumers feel about a new product or predicting election outcomes based on public sentiment. The document also discusses challenges in modeling affective meaning in language at the lexical level in order to perform tasks like sentiment analysis.
Learning to understand phrases by embedding the dictionaryRoelof Pieters
The document describes a model that uses an RNN with LSTM cells to learn useful representations of phrases by mapping dictionary definitions to word embeddings, addressing the gap between lexical and phrasal semantics. The model is applied to two tasks: a reverse dictionary/concept finder that takes phrases as input and outputs words, and a general knowledge question answering system for crosswords. The RNN is trained on dictionary definitions to map phrases to target word embeddings, then tested on new input phrases.
This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
Words and sentences are the basic units of text. In this lecture we discuss basics of operations on words and sentences such as tokenization, text normalization, tf-idf, cosine similarity measures, vector space models and word representation
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
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.
The document provides an overview of natural language processing (NLP). It defines NLP as the automatic processing of human language and discusses how NLP relates to fields like linguistics, cognitive science, and computer science. The document also describes common NLP tasks like information extraction, machine translation, and summarization. It discusses challenges in NLP like ambiguity and examines techniques used in NLP like rule-based systems, probabilistic models, and the use of linguistic knowledge.
Introduction to natural language processingMinh Pham
This document provides an introduction to natural language processing (NLP). It discusses what NLP is, why NLP is a difficult problem, the history of NLP, fundamental NLP tasks like word segmentation, part-of-speech tagging, syntactic analysis and semantic analysis, and applications of NLP like information retrieval, question answering, text summarization and machine translation. The document aims to give readers an overview of the key concepts and challenges in the field of natural language processing.
Adnan: Introduction to Natural Language Processing Mustafa Jarrar
This document provides an introduction to natural language processing (NLP). It discusses key topics in NLP including languages and intelligence, the goals of NLP, applications of NLP, and general themes in NLP like ambiguity in language and statistical vs rule-based methods. The document also previews specific NLP techniques that will be covered like part-of-speech tagging, parsing, grammar induction, and finite state analysis. Empirical approaches to NLP are discussed including analyzing word frequencies in corpora and addressing data sparseness issues.
NLP Bootcamp 2018 : Representation Learning of text for NLPAnuj Gupta
The document provides an outline for a workshop on representation learning of text for natural language processing (NLP). The workshop will be divided into 4 modules covering both foundational techniques like one-hot encoding and bag-of-words as well as state-of-the-art methods like word, sentence, and character vectors. The objective is for participants to gain a deeper understanding of the key ideas, math, and code behind text representation techniques in order to apply them to solve NLP problems and achieve higher accuracies and understanding.
This document provides an overview of deep learning techniques for natural language processing (NLP). It discusses some of the challenges in language understanding like ambiguity and productivity. It then covers traditional ML approaches to NLP problems and how deep learning improves on these approaches. Some key deep learning techniques discussed include word embeddings, recursive neural networks, and language models. Word embeddings allow words with similar meanings to have similar vector representations, improving tasks like sentiment analysis. Recursive neural networks can model hierarchical structures like sentences. Language models assign probabilities to word sequences.
The document discusses two paradigms for natural language processing: knowledge engineering and machine learning. It provides examples of how each approach handles tasks like parsing, translation, and question formation. While knowledge engineering relies on hand-coded rules and representations, machine learning trains statistical models on large datasets. The document also notes Microsoft's interests in using NLP for applications like search and summarization.
Natural Language Processing for Games ResearchJose Zagal
This document discusses how natural language processing (NLP) techniques can help analyze large amounts of text data from games to aid research in game studies. It provides examples of using NLP for part-of-speech tagging, syntactic parsing, and analyzing game reviews and player language to study gameplay descriptions. The document argues that NLP allows researchers to verify hypotheses and explore new questions at a scale not previously possible by automatically processing vast amounts of game text data.
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
The document discusses several key topics in natural language processing and computational linguistics:
1. It defines the basic units of language like words, tokens, types and texts.
2. It describes techniques for extracting text from various sources like files, web pages and corpora and preprocessing the text by removing HTML tags and normalizing whitespace.
3. It discusses empirical observations about word frequencies like Zipf's Law and Heap's Law, which state that a small number of words occur very frequently while most words occur rarely.
Jarrar: Introduction to Natural Language ProcessingMustafa Jarrar
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=aNpLekq6-oA&list=PL44443F36733EF123
This document provides an outline on natural language processing and machine vision. It begins with an introduction to different levels of natural language analysis, including phonetic, syntactic, semantic, and pragmatic analysis. Phonetic analysis constructs words from phonemes using frequency spectrograms. Syntactic analysis builds a structural description of sentences through parsing. Semantic analysis generates a partial meaning representation from syntax, while pragmatic analysis uses context. The document also introduces machine vision as a technology using optical sensors and cameras for industrial quality control through detection of faults. It operates through sensing images, processing/analyzing images, and various applications.
Incorporating word reordering knowledge into attention-based neural machine t...sekizawayuuki
The document proposes a method to incorporate word reordering knowledge into attention-based neural machine translation using a distortion model. The method extends the attention mechanism to consider both the semantic requirements and a word reordering penalty. It achieves state-of-the-art performance on translation quality and improves word alignment quality compared to baseline neural machine translation and prior work.
Engineering Intelligent NLP Applications Using Deep Learning – Part 2 Saurabh Kaushik
This document discusses how deep learning techniques can be applied to natural language processing tasks. It begins by explaining some of the limitations of traditional rule-based and machine learning approaches to NLP, such as the lack of semantic understanding and difficulty of feature engineering. Deep learning approaches can learn features automatically from large amounts of unlabeled text and better capture semantic and syntactic relationships between words. Recurrent neural networks are well-suited for NLP because they can model sequential data like text, and convolutional neural networks can learn hierarchical patterns in text.
This document provides an overview of sentiment analysis and discusses why it is an important area of research in language technology. Sentiment analysis involves detecting positive or negative opinions in text about products, politicians, or other topics. It has many applications, such as determining how consumers feel about a new product or predicting election outcomes based on public sentiment. The document also discusses challenges in modeling affective meaning in language at the lexical level in order to perform tasks like sentiment analysis.
Learning to understand phrases by embedding the dictionaryRoelof Pieters
The document describes a model that uses an RNN with LSTM cells to learn useful representations of phrases by mapping dictionary definitions to word embeddings, addressing the gap between lexical and phrasal semantics. The model is applied to two tasks: a reverse dictionary/concept finder that takes phrases as input and outputs words, and a general knowledge question answering system for crosswords. The RNN is trained on dictionary definitions to map phrases to target word embeddings, then tested on new input phrases.
This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
Words and sentences are the basic units of text. In this lecture we discuss basics of operations on words and sentences such as tokenization, text normalization, tf-idf, cosine similarity measures, vector space models and word representation
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
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.
The document provides an overview of natural language processing (NLP). It defines NLP as the automatic processing of human language and discusses how NLP relates to fields like linguistics, cognitive science, and computer science. The document also describes common NLP tasks like information extraction, machine translation, and summarization. It discusses challenges in NLP like ambiguity and examines techniques used in NLP like rule-based systems, probabilistic models, and the use of linguistic knowledge.
Introduction to natural language processingMinh Pham
This document provides an introduction to natural language processing (NLP). It discusses what NLP is, why NLP is a difficult problem, the history of NLP, fundamental NLP tasks like word segmentation, part-of-speech tagging, syntactic analysis and semantic analysis, and applications of NLP like information retrieval, question answering, text summarization and machine translation. The document aims to give readers an overview of the key concepts and challenges in the field of natural language processing.
Adnan: Introduction to Natural Language Processing Mustafa Jarrar
This document provides an introduction to natural language processing (NLP). It discusses key topics in NLP including languages and intelligence, the goals of NLP, applications of NLP, and general themes in NLP like ambiguity in language and statistical vs rule-based methods. The document also previews specific NLP techniques that will be covered like part-of-speech tagging, parsing, grammar induction, and finite state analysis. Empirical approaches to NLP are discussed including analyzing word frequencies in corpora and addressing data sparseness issues.
NLP Bootcamp 2018 : Representation Learning of text for NLPAnuj Gupta
The document provides an outline for a workshop on representation learning of text for natural language processing (NLP). The workshop will be divided into 4 modules covering both foundational techniques like one-hot encoding and bag-of-words as well as state-of-the-art methods like word, sentence, and character vectors. The objective is for participants to gain a deeper understanding of the key ideas, math, and code behind text representation techniques in order to apply them to solve NLP problems and achieve higher accuracies and understanding.
This document provides an overview of deep learning techniques for natural language processing (NLP). It discusses some of the challenges in language understanding like ambiguity and productivity. It then covers traditional ML approaches to NLP problems and how deep learning improves on these approaches. Some key deep learning techniques discussed include word embeddings, recursive neural networks, and language models. Word embeddings allow words with similar meanings to have similar vector representations, improving tasks like sentiment analysis. Recursive neural networks can model hierarchical structures like sentences. Language models assign probabilities to word sequences.
The document discusses two paradigms for natural language processing: knowledge engineering and machine learning. It provides examples of how each approach handles tasks like parsing, translation, and question formation. While knowledge engineering relies on hand-coded rules and representations, machine learning trains statistical models on large datasets. The document also notes Microsoft's interests in using NLP for applications like search and summarization.
Natural Language Processing for Games ResearchJose Zagal
This document discusses how natural language processing (NLP) techniques can help analyze large amounts of text data from games to aid research in game studies. It provides examples of using NLP for part-of-speech tagging, syntactic parsing, and analyzing game reviews and player language to study gameplay descriptions. The document argues that NLP allows researchers to verify hypotheses and explore new questions at a scale not previously possible by automatically processing vast amounts of game text data.
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
The document discusses several key topics in natural language processing and computational linguistics:
1. It defines the basic units of language like words, tokens, types and texts.
2. It describes techniques for extracting text from various sources like files, web pages and corpora and preprocessing the text by removing HTML tags and normalizing whitespace.
3. It discusses empirical observations about word frequencies like Zipf's Law and Heap's Law, which state that a small number of words occur very frequently while most words occur rarely.
Jarrar: Introduction to Natural Language ProcessingMustafa Jarrar
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=aNpLekq6-oA&list=PL44443F36733EF123
This document provides an outline on natural language processing and machine vision. It begins with an introduction to different levels of natural language analysis, including phonetic, syntactic, semantic, and pragmatic analysis. Phonetic analysis constructs words from phonemes using frequency spectrograms. Syntactic analysis builds a structural description of sentences through parsing. Semantic analysis generates a partial meaning representation from syntax, while pragmatic analysis uses context. The document also introduces machine vision as a technology using optical sensors and cameras for industrial quality control through detection of faults. It operates through sensing images, processing/analyzing images, and various applications.
Incorporating word reordering knowledge into attention-based neural machine t...sekizawayuuki
The document proposes a method to incorporate word reordering knowledge into attention-based neural machine translation using a distortion model. The method extends the attention mechanism to consider both the semantic requirements and a word reordering penalty. It achieves state-of-the-art performance on translation quality and improves word alignment quality compared to baseline neural machine translation and prior work.
Tomoyuki Kajiwara, Kazuhide Yamamoto.
Noun Paraphrasing Based on a Variety of Contexts.
In Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation (PACLIC 28), pp.644-649. Phuket, Thailand, December 2014.
文献紹介:Simple English Wikipedia: A New Text Simplification TaskTomoyuki Kajiwara
William Coster, David Kauchak. Simple wikipedia: A new simplification task. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp.665–669, 2011.
The document analyzes how the lexicon (identifiers) and structure of programs evolve over multiple versions of three software systems: Eclipse, Mozilla, and CERN/Alice. It finds that the lexicon is generally more stable than structure and that renaming of identifiers is rare. Some reasons why the lexicon is reluctant to change include the cognitive burden of changes and lack of dedicated renaming tools. The study concludes that more research is needed on tools to help preserve and improve a program's lexicon over time.
IRJET - Response Analysis of Educational VideosIRJET Journal
This document summarizes a research paper that analyzes student feedback on educational videos through sentiment analysis. It proposes a system to collect student comments, preprocess the data, identify sentiment and emotions, compute student satisfaction and dissatisfaction, and visualize the results. The system uses machine learning techniques like term frequency-inverse document frequency and random forest classification. It achieved 62.5% accuracy in classifying sentiment polarity in student comments. The analysis of student responses can help teachers better understand student interest and identify areas for improvement.
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.
Poster: Controlled and Balanced Dataset for Japanese Lexical SimplificationKodaira Tomonori
This document presents a new controlled and balanced dataset for Japanese lexical simplification. The dataset contains 2,100 sentences each with a single difficult Japanese word. Five annotators provided substitution options for each complex word and ranked them in order of simplification. This dataset is the first for Japanese lexical simplification to only allow one complex word per sentence and include particles, resulting in higher correlation with human judgment than prior datasets. It will enable better machine learning methods for Japanese lexical simplification.
Examining the Impact of Individual Differences of Informatijon Processing St...Takeshi Sato
Many studies have been conducted to verify the effectiveness of technology-enhanced visual aids in second language learning, and conclude the positive effects of the aids both in incidental and intentional vocabulary learning. On the other hand, previous research by Sato & Suzuki (2010, 2011, 2012) to compare the effectiveness of still pictures with animations depicting the schematic images of English prepositions found no significant difference between the pictorial and animated images. This indicates that successful second vocabulary learning with technological aids results not only from the technology itself, but the individual factors of the learners who use the technology. This study, therefore, explores the individual factors that affect the learning of prepositions through the use of animations, focusing on information processing styles and the first language of the learners. The results of our research conducted both in Taiwan and Japan show that the Taiwanese received a positive effect in the post-test administered immediately after using the visual aids whereas the Japanese received a positive effect in the delayed-test two weeks later. Besides, the imagers, who prefer using images in processing information, tend to get better results than the verbalizers, who prefer using languages in their information processing, whether they are Taiwanese or Japanese. From these findings, we conclude the importance of individual factors in examining second vocabulary learning with technology.
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding D...Jinho Choi
Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resources while keeping high accuracy. This paper touches the core of neural models in NLP, word embeddings, and presents a new embedding distillation framework that remarkably reduces the dimension of word embeddings without compromising accuracy. A novel distillation ensemble approach is also proposed that trains a high-efficient student model using multiple teacher models. In our approach, the teacher models play roles only during training such that the student model operates on its own without getting supports from the teacher models during decoding, which makes it eighty times faster and lighter than other typical ensemble methods. All models are evaluated on seven document classification datasets and show significant advantage over the teacher models for most cases. Our analysis depicts insightful transformation of word embeddings from distillation and suggests a future direction to ensemble approaches using neural models.
The document describes the Columbia-GWU system submitted to the 2016 TAC KBP BeSt Evaluation. It discusses several approaches used for different languages and genres, including:
1) A sentiment system based on identifying the target only, adapted for English, Chinese, and Spanish.
2) An English sentiment system based on relation extraction, treating sentiment as a relation between source and target.
3) English and Chinese belief systems that combine high-precision word tagging with a high-recall default system.
4) A Spanish belief system based on weighted random choice of tags.
The document provides details on the data, approaches, and results for each language-specific system.
This document summarizes Jessica Hullman's project modeling word sense disambiguation using support vector machines. She used a dataset from Senseval-2 and achieved an average accuracy of 87% at assigning word senses, with a standard deviation of 15% and median of 92%. The project involved modifying an existing implementation that used part-of-speech tags of neighboring words to classify word senses, training support vector machine classifiers on the Senseval-2 data.
This document provides an overview of the CS447: Natural Language Processing course at the University of Illinois. It discusses the following topics:
- The course schedule, including lectures on neural approaches to NLP like word embeddings and recurrent neural networks.
- Two core problems in NLP: ambiguity and coverage due to rare or unseen words.
- How statistical models are used to handle these problems through probabilistic modeling and machine learning techniques.
- The limitations of traditional NLP models like n-grams that make strong independence assumptions, motivating neural approaches.
- An introduction to neural networks and their use in applications like language modeling, word embeddings, sequence-to-sequence models, and recursive neural networks.
This document summarizes a presentation about a sentiment analysis system developed for a large Korean telecommunications company. The system was designed to analyze customer feedback from call centers. It classified feedback into categories, identified trends over time, and detected complaints. The system used Korean linguistic analysis and sentiment classification. It showed the benefits of combining machine learning and rules-based approaches. However, challenges remained around data quality, lexicon development, and meeting customer expectations. Future work focused on improving the sentiment dictionary and developing a platform for ongoing natural language processing services.
The Ins and Outs of Preposition Semantics: Challenges in Comprehensive Corpu...Seth Grimes
Presentation by Nathan Scheider, Georgetown University, to the Washington DC Natural Language Processing meetup, October 14, 2019, https://www.meetup.com/DC-NLP/events/264894589/.
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...Seth Grimes
The document summarizes Nathan Schneider's presentation on preposition semantics. It discusses challenges in annotating prepositions in corpora and approaches to their semantic description and disambiguation. It presents Schneider's work on developing a unified semantic scheme for prepositions and possessives consisting of 50 semantic classes applied to a corpus of English web reviews. Inter-annotator agreement for the new corpus was 78%. Models for preposition disambiguation were evaluated, with the feature-rich linear model achieving the highest accuracy of 80%.
Lexical Analysis to Effectively Detect User's Opinion dannyijwest
In this paper we present a lexical approach that will identify opinion of web users popularly expressed
using short words or sms words. These words are pretty popular with diverse web users and are used for
expressing their opinion on the web. The study of opinion from web arises to know the diverse opinion of
web users. The opinion expressed by web users may be on diverse topics such as politics, sports, products,
movies etc. These opinions will be very useful to others such as, leaders of political parties, selection
committees of various sports, business analysts and other stake holders of products, directors and
producers of movies as well as to the other concerned web users. We use semantic based approach to find
users opinion from short words or sms words apart of regular opinionated phrases. Our approach
efficiently detects opinion from opinionated texts using lexical analysis and is found to be better than the
other approaches on different data sets.
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
This document provides an overview of natural language processing (NLP) and the use of deep learning for NLP tasks. It discusses how deep learning models can learn representations and patterns from large amounts of unlabeled text data. Deep learning approaches are now achieving superior results to traditional NLP methods on many tasks, such as named entity recognition, machine translation, and question answering. However, deep learning models do not explicitly model linguistic knowledge. The document outlines common NLP tasks and how deep learning algorithms like LSTMs, CNNs, and encoder-decoder models are applied to problems involving text classification, sequence labeling, and language generation.
How can text-mining leverage developments in Deep Learning? Presentation at ...jcscholtes
How can text-mining leverage developments in Deep Learning?
Text-mining focusses primary on extracting complex patterns from unstructured electronic data sets and applying machine learning for document classification. During the last decade, a generation of efficient and successful algorithms has been developed using bag-of-words models to represent document content and statistical and geometrical machine learning algorithms such as Conditional Random Fields and Support Vector Machines. These algorithms require relatively little training data and are fast on modern hardware. However, performance seems to be stuck around 90% F1 values.
In computer vision, deep learning has shown great success where the 90% barrier has been broken in many application. In addition, deep learning also shows new successes for transfer learning and self-learning such as reinforcement leaning. Dedicated hardware helped us to overcome computational challenges and methods such as training data augmentation solved the need for unrealistically large data sets.
So, it would make sense to apply deep learning also on textual data as well. But how do we represent textual data: there are many different methods for word embeddings and as many deep learning architectures. Training data augmentation, transfer learning and reinforcement leaning are not fully defined for textual data.
Deep learning for natural language embeddingsRoelof Pieters
This document discusses approaches to understanding natural language through deep learning techniques. It begins by outlining some of the challenges of language understanding, such as ambiguity and productivity. It then discusses using neural networks for natural language processing tasks like language modeling, sentiment analysis and machine translation. Recurrent and recursive neural networks are presented as approaches to model the compositionality of language. Different methods for obtaining word embeddings like Word2Vec, GloVe and earlier distributional semantic models are also summarized.
The document summarizes a presentation given at the GLoCALL 2013 conference on using visual aids to enhance L2 vocabulary learning. The presentation discussed previous research finding pictorial and video glosses effective for intentional learning. It outlined a study examining the effectiveness of pictorial vs. live-action images for learning spatial prepositions incidentally. The study found no significant difference in learning between image types. Both image conditions led to significant gains from pre- to post-test, suggesting images can facilitate preposition learning regardless of technological complexity.
Neural Text Embeddings for Information Retrieval (WSDM 2017)Bhaskar Mitra
The document describes a tutorial on using neural networks for information retrieval. It discusses an agenda for the tutorial that includes fundamentals of IR, word embeddings, using word embeddings for IR, deep neural networks, and applications of neural networks to IR problems. It provides context on the increasing use of neural methods in IR applications and research.
Similar to Evaluation Dataset and System for Japanese Lexical Simplification (20)
文献紹介:SemEval-2012 Task 1: English Lexical SimplificationTomoyuki Kajiwara
Lucia Specia, Sujay Kumar Jauhar, Rada Mihalcea. SemEval-2012 Task 1: English Lexical Simplification. In Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval-2012), pp.347-355, 2012.
Tomoyuki Kajiwara, Hiroshi Matsumoto, Kazuhide Yamamoto.
Selecting Proper Lexical Paraphrase for Children.
In Proceedings of the 25th Conference on Computational Linguistics and Speech Processing (ROCLING 2013), pp.769-772. Kaohsiung, Taiwan, October 2013.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
Evaluation Dataset and System for Japanese Lexical Simplification
1. Evaluation Dataset and System
for Japanese Lexical Simplification
Tomoyuki Kajiwara and Kazuhide Yamamoto
Nagaoka University of Technology, Japan
(Now I am studying at the Tokyo Metropolitan University)
2. Extensive / various forms of texts
Hitler committed terrible atrocities
during the second World War.
Hitler committed terrible cruelties
during the second World War.
Children Language Learners Elderlies
Motivation
Easily
accessible
Easily
readable and
understandable
too!
3. Problems in Japanese
Ø Unpublished system
• It is difficult for people who need reading
assistance to obtain simple Japanese sentences
Ø Unpublished dataset
• It is difficult for researchers and developers to
evaluate the performance of different systems
4. Our works
ü Built and published
Japanese lexical simplification system
http://www.jnlp.org/SNOW/S3
ü Built and published dataset for
evaluation of Japanese lexical simplification
http://www.jnlp.org/SNOW/E4
5. Lexical Simplification System
Substitution Generation
担う: 支える,引継ぐ,受け継ぐ,伝承する
bear: hold, wear, carry, expect
Identification of Complex Words
担う
bear
Word Sense Disambiguation
担う: 支える, 受け継ぐ
bear: hold, carry
Synonym Ranking
1: 支える, 2: 受け継ぐ, 3: 担う
1: hold, 2: carry, 3: bear
Input
未来は若者が担う
Young people bear the future
Output
未来は若者が支える
Young people hold the future
6. Lexical Simplification Dataset
1. Constructing Japanese Lexical Substitution Dataset
・Collecting Substitutions (crowdsourcing)
・Evaluating Substitutions (crowdsourcing)
2. Transforming into Lexical Simplification Dataset
・Ranking Substitutions (crowdsourcing)
・Merging All Rankings
Sample: Young people bear the future.(未来は若者が担う)
Lexical Substitutions: carry, hold (受け継ぐ, 支える)
Rank of Simple Level: 1.hold, 2.carry, 3.bear
(1. 支える, 2. 受け継ぐ, 3. 担う)
7. Evaluation
Dataset Sentence Noun Verb Adjective Adverb
SemEval [1]
2012 Task1
2,010
580
(28.9%)
520
(25.9%)
560
(27.9%)
350
(17.4%)
Ours 2,330
630
(27.0%)
720
(30.9%)
500
(21.5%)
480
(20.6%)
System Precision Recall F-measure
Our Original 0.89 0.08 0.15
w/o WSD 0.84 0.71 0.77
[1] Lucia Specia, Sujay Kumar Jauhar, and Rada Mihalcea. 2012.
[1] Semeval-2012 task 1: English lexical simplification.
[1] In Proceedings of the 6th International Workshop on Semantic Evaluation, pages 347‒355.
8. We built and published system and evaluation
dataset for Japanese lexical simplification
http://www.jnlp.org/SNOW
Lexical Simplification:
• Substitutes a complex word or phrase
in a sentence with a simpler synonym
• Supports the reading comprehension
of a wide range of readers (e.g. language learners)
Evaluation Dataset and System
for Japanese Lexical Simplification
Tomoyuki Kajiwara and Kazuhide Yamamoto
Nagaoka University of Technology, Japan