Recovering Diacritics using Wikipedia and Google

This document summarizes a paper on using simple lexical overlap features with support vector machines (SVMs) for Russian paraphrase identification. It introduces paraphrase identification and various paraphrase corpora. It then describes a knowledge-lean approach using only tokenization, lowercasing, and overlap features like union and intersection size as inputs to linear and RBF kernel SVMs. The method achieves competitive results on English, Turkish, and Russian paraphrase identification tasks.

Lecture 2: Computational Semantics

Word Embeddings - Introduction

Christian Perone

The document provides an introduction to word embeddings and two related techniques: Word2Vec and Word Movers Distance. Word2Vec is an algorithm that produces word embeddings by training a neural network on a large corpus of text, with the goal of producing dense vector representations of words that encode semantic relationships. Word Movers Distance is a method for calculating the semantic distance between documents based on the embedded word vectors, allowing comparison of documents with different words but similar meanings. The document explains these techniques and provides examples of their applications and properties.

Word Embedding to Document distances

Ganesh Borle

From Word Embeddings To Document Distances We present the Word Mover’s Distance (WMD), a novel distance function between text documents. Our work is based on recent results in word embeddings that learn semantically meaningful representations for words from local cooccurrences in sentences. The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to “travel” to reach the embedded words of another document. We show that this distance metric can be cast as an instance of the Earth Mover’s Distance, a well studied transportation problem for which several highly efficient solvers have been developed. Our metric has no hyperparameters and is straight-forward to implement. Further, we demonstrate on eight real world document classification data sets, in comparison with seven state-of-the-art baselines, that the WMD metric leads to unprecedented low k-nearest neighbor document classification error rates.

Lecture: Vector Semantics (aka Distributional Semantics)

This document discusses techniques for semantic analysis in natural language processing using distributional semantics or vector space models. It describes how words can be represented as vectors based on their collocational features or surrounding words within a window. It also discusses using bag-of-words features to represent words based on a predefined vocabulary. Finally, it explains Lesk algorithms for word sense disambiguation, which compare the signatures of target words and context words based on dictionary definitions and corpus examples.

What is word2vec?

Traian Rebedea

This document provides an overview of natural language processing (NLP) including: 1. An introduction to NLP and its intersection with computational linguistics, computer science, and statistics. 2. A discussion of common NLP problems like tokenization, tagging, parsing, and their rule-based and statistical approaches. 3. An explanation of machine learning techniques for NLP like language models, naive Bayes classifiers, and dependency parsing. 4. Steps for developing an NLP system including translating requirements, experimentation, and going to production.

Tomáš Mikolov - Distributed Representations for NLP

Machine Learning Prague

The document discusses word embedding techniques, specifically Word2vec. It introduces the motivation for distributed word representations and describes the Skip-gram and CBOW architectures. Word2vec produces word vectors that encode linguistic regularities, with simple examples showing words with similar relationships have similar vector offsets. Evaluation shows Word2vec outperforms previous methods, and its word vectors are now widely used in NLP applications.

Word Embeddings, why the hype ?

Hady Elsahar

Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks. In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix. Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings. Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.

AINL 2016: Yagunova

The document describes a Russian paraphrase corpus created by the authors. It contains over 8000 sentence pairs annotated as precise, loose, or non-paraphrases using crowdsourcing. The corpus was collected from news headlines and aims to capture the most important events. The authors evaluate different models for classifying sentence pairs and find that combining linguistic features improves performance over individual feature types. Graphs built from the corpus can reveal connected events more completely than human annotations alone.

Thai Word Embedding with Tensorflow

Kobkrit Viriyayudhakorn

This document discusses using word embeddings and the Word2Vec model for natural language processing tasks in TensorFlow. It explains that word embeddings are needed to understand relationships between words as text, unlike images, does not provide inherent relationships between symbols like pixels. Word2Vec is an efficient predictive model that learns word embeddings from raw text using either the Continuous Bag-of-Words or Skip-Gram architecture and negative sampling to discriminate real from imaginary words during training. The tutorial aims to teach how to perform NLP tasks in TensorFlow using Word2Vec to learn word embeddings from a text corpus.

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.

L3 v2

This document is the slides for a lecture on part-of-speech tagging, keyword and phrase extraction, and text similarity for natural language processing. It introduces part-of-speech tagging and different taggers such as rule-based and ngram-based approaches. It also discusses methods for keyword and phrase extraction including supervised classifiers and unsupervised techniques like TF-IDF. Finally, it covers measuring text similarity using vector space models and cosine similarity.

Intro to NLP. Lecture 2

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.

Word representations in vector space

Abdullah Khan Zehady

- The document discusses neural word embeddings, which represent words as dense real-valued vectors in a continuous vector space. This allows words with similar meanings to have similar vector representations. - It describes how neural network language models like skip-gram and CBOW can be used to efficiently learn these word embeddings from unlabeled text data in an unsupervised manner. Techniques like hierarchical softmax and negative sampling help reduce computational complexity. - The learned word embeddings show meaningful syntactic and semantic relationships between words and allow performing analogy and similarity tasks without any supervision during training.

GDG Tbilisi 2017. Word Embedding Libraries Overview: Word2Vec and fastText

rudolf eremyan

A general method applicable to the search for anglicisms in russian social ne...

Ilia Karpov

In the process of globalization, the number of English words in other languages has rapidly increased. In automatic speech recognition systems, spell-checking, tagging, and other software in the field of natural language processing, loan words are not easily recognized and should be evaluated separately. In this paper we present a corpora-based approach to the automatic detection of anglicisms in Russian social network texts. Proposed method is based on the idea of simultaneous scripting, phonetics, and semantics similarity of the original Latin word and its Cyrillic analogue. We used a set of transliteration, phonetic transcribing, and morphological analysis methods to find possible hypotheses and distributional semantic models to filter them. Resulting list of borrowings, gathered from approximately 20 million LiveJournal texts, shows good intersection with manually collected dictionary. Proposed method is fully automated and can be applied to any domain–specific area. Full paper available at: https://www.academia.edu/29834070/A_General_Method_Applicable_to_the_Search_for_Anglicisms_in_Russian_Social_Network_Texts

Semantic Role Labeling

Text Mining for Lexicography

Leiden University

Suzan Verberne gave a workshop on using text mining for lexicography. She discussed using word embeddings to help discover and select new lemmas for dictionaries. Word2Dict is a lexicographic tool that uses word embeddings to present words semantically related to the lemma being described. Word embeddings learn dense vector representations of words by predicting words in context using neural networks, improving on the traditional sparse vector space model. Word embeddings can be trained using the Word2Vec algorithm and analyzed using the Gensim Python package to gain linguistic insights and improve natural language processing applications.

AINL 2016: Malykh

V. Malykh presents an approach for creating robust word vectors for the Russian language that does not rely on a predefined vocabulary or word co-occurrence matrices. The approach uses a LSTM neural network and BME representations of words at the character level to learn word embeddings. Experiments on Russian corpora for paraphrase identification and plagiarism detection show the approach outperforms standard word2vec models, especially in noisy conditions with character substitutions and additions/deletions.

Representation Learning of Vectors of Words and Phrases

Felipe Moraes

Lecture: Word Senses

Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...

Marcin Junczys-Dowmunt

Video recording of full talk: http://lectures.ms.mff.cuni.cz/view.php?rec=255 There has been an increasing interest in using statistical machine translation (SMT) for the task of Grammatical Error Correction (GEC) for English-as-a-Second-Language (ESL) learners. Two of the three highest-scoring systems of the CoNLL-2014 Shared Task were SMT-based. The currently highest-scoring result published for the CoNLL-2014 test set has been achieved by a system combination of the five best CoNLL-2014 submissions built with MEMT (a tool for MT system combination). In this talk, we demonstrate how a single SMT-based system can match and outperform the result of the mentioned combined system. Furthermore, this system outperforms any other published results (including our own CoNLL-2014 submission) for a single system by a margin of several percent F-Score when the same training data is being used. These results are achieved by adapting current state-of-the art methods for phrase-based SMT specifically to the problem of GEC. We report on the effects of: - Parameter tuning for GEC - Introducing GEC-specific dense and sparse features - Using large-scale data

New word analogy corpus

Lukáš Svoboda

The document introduces a new word analogy corpus for the Czech language to evaluate word embedding models. It contains over 22,000 semantic and syntactic analogy questions across various categories. The authors experiment with Word2Vec (CBOW and Skip-gram) and GloVe models on the Czech Wikipedia corpus. Their results show that CBOW generally performs best, with accuracy improving as vector dimension and training epochs increase. Performance is better on syntactic tasks than semantic ones. The new Czech analogy corpus allows further exploration of how word embeddings represent semantics and syntax for highly inflected languages.

ورشة تضمين الكلمات في التعلم العميق Word embeddings workshop

iwan_rg

This document provides an introduction to word embeddings in deep learning. It defines word embeddings as vectors of real numbers that represent words, where similar words have similar vector representations. Word embeddings are needed because they allow words to be treated as numeric inputs for machine learning algorithms. The document outlines different types of word embeddings, including frequency-based methods like count vectors and co-occurrence matrices, and prediction-based methods like CBOW and skip-gram models from Word2Vec. It also discusses tools for generating word embeddings like Word2Vec, GloVe, and fastText. Finally, it provides a tutorial on implementing Word2Vec in Python using Gensim.

Phonetic 2

Fatmawati Khodijah

IPA (International Phonetic Alphabet) was established by the International Phonetic Association in 1886 to standardize phonetic symbols across languages and enable accurate communication about pronunciation. It is used extensively by linguists, speech pathologists, and others for in-depth study of pronunciation. The IPA has expanded over time to cover additional sounds and languages, with major revisions occurring in 1900 to include Arabic sounds and 1932 to incorporate additional symbols.

Ipa internacional phonetic_alphabet

Luciana Viter

What's hot

Tutorial on word2vec

Leiden University

Crash Course in Natural Language Processing (2016)

Vsevolod Dyomkin

Tomáš Mikolov - Distributed Representations for NLP

Machine Learning Prague

Word Embeddings, why the hype ?

Hady Elsahar

AINL 2016: Yagunova

Thai Word Embedding with Tensorflow

Kobkrit Viriyayudhakorn

L3 v2

Intro to NLP. Lecture 2

Word representations in vector space

Abdullah Khan Zehady

GDG Tbilisi 2017. Word Embedding Libraries Overview: Word2Vec and fastText

rudolf eremyan

A general method applicable to the search for anglicisms in russian social ne...

Ilia Karpov

Semantic Role Labeling

Text Mining for Lexicography

Leiden University

AINL 2016: Malykh

Representation Learning of Vectors of Words and Phrases

Felipe Moraes

Lecture: Word Senses