scholarly journals A Polarity Capturing Sphere for Word to Vector Representation

2020 ◽  
Vol 10 (12) ◽  
pp. 4386 ◽  
Author(s):  
Sandra Rizkallah ◽  
Amir F. Atiya ◽  
Samir Shaheen
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
State Of The Art ◽  
Unrelated Word ◽  
Research Field ◽  
Word Embedding ◽  
Vector Representation ◽  
Active Research ◽  
Embedding Methods ◽  
Better Than

Embedding words from a dictionary as vectors in a space has become an active research field, due to its many uses in several natural language processing applications. Distances between the vectors should reflect the relatedness between the corresponding words. The problem with existing word embedding methods is that they often fail to distinguish between synonymous, antonymous, and unrelated word pairs. Meanwhile, polarity detection is crucial for applications such as sentiment analysis. In this work we propose an embedding approach that is designed to capture the polarity issue. The approach is based on embedding the word vectors into a sphere, whereby the dot product between any vectors represents the similarity. Vectors corresponding to synonymous words would be close to each other on the sphere, while a word and its antonym would lie at opposite poles of the sphere. The approach used to design the vectors is a simple relaxation algorithm. The proposed word embedding is successful in distinguishing between synonyms, antonyms, and unrelated word pairs. It achieves results that are better than those of some of the state-of-the-art techniques and competes well with the others.

scholarly journals A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Bingkun Wang ◽  
Yongfeng Huang ◽  
Xian Wu ◽  
Xing Li
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Negative Polarity ◽  
Fuzzy Classifier ◽  
Important Indicator ◽  
Computing Model ◽  
Active Research ◽  
Better Than ◽  
Fuzzy Computing

With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods.

scholarly journals Multi-Sense Embeddings per Word

2020 ◽  
Author(s):  
Masashi Sugiyama
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Area ◽  
Word Embedding ◽  
The Other ◽  
Word Embeddings ◽  
Word Similarity ◽  
Better Than ◽  
Non Parametric

Recently, word embeddings have been used in many natural language processing problems successfully and how to train a robust and accurate word embedding system efficiently is a popular research area. Since many, if not all, words have more than one sense, it is necessary to learn vectors for all senses of word separately. Therefore, in this project, we have explored two multi-sense word embedding models, including Multi-Sense Skip-gram (MSSG) model and Non-parametric Multi-sense Skip Gram model (NP-MSSG). Furthermore, we propose an extension of the Multi-Sense Skip-gram model called Incremental Multi-Sense Skip-gram (IMSSG) model which could learn the vectors of all senses per word incrementally. We evaluate all the systems on word similarity task and show that IMSSG is better than the other models.

scholarly journals NMT Multi-Sense Embeddings per Word

2019 ◽  
Author(s):  
William Jin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Area ◽  
Word Embedding ◽  
The Other ◽  
Word Embeddings ◽  
Word Similarity ◽  
Better Than ◽  
Non Parametric

Recently, word embeddings have been used in many natural language processing problems successfully and how to train a robust and accurate word embedding system efficiently is a popular research area. Since many, if not all, words have more than one sense, it is necessary to learn vectors for all senses of word separately. Therefore, in this project, we have explored two multi-sense word embedding models, including Multi-Sense Skip-gram (MSSG) model and Non-parametric Multi-sense Skip Gram model (NP-MSSG). Furthermore, we propose an extension of the Multi-Sense Skip-gram model called Incremental Multi-Sense Skip-gram (IMSSG) model which could learn the vectors of all senses per word incrementally. We evaluate all the systems on word similarity task and show that IMSSG is better than the other models.

scholarly journals Mention Recommendation for Twitter with End-to-end Memory Network

2017 ◽  
Author(s):  
Haoran Huang ◽  
Qi Zhang ◽  
Xuanjing Huang
Keyword(s):  
Statistical Learning ◽  
Language Processing ◽  
Network Architecture ◽  
State Of The Art ◽  
Rapid Development ◽  
Word Embedding ◽  
Social Networking Services ◽  
Memory Network ◽  
End To End ◽  
Embedding Methods

In this study, we investigated the problem of recommending usernames when people attempt to use the ``@'' sign to mention other people in twitter-like social media. With the extremely rapid development of social networking services, this problem has received considerable attention in recent years. Previous methods have studied the problem from different aspects. Because most of Twitter-like microblogging services limit the length of posts, statistical learning methods may be affected by the problems of word sparseness and synonyms. Although recent progress in neural word embedding methods have advanced the state-of-the-art in many natural language processing tasks, the benefits of word embedding have not been taken into consideration for this problem. In this work, we proposed a novel end-to-end memory network architecture to perform this task. We incorporated the interests of users with external memory. A hierarchical attention mechanism was also applied to better consider the interests of users. The experimental results on a dataset we collected from Twitter demonstrated that the proposed method could outperform state-of-the-art approaches.

scholarly journals Enriching Word Vectors with Subword Information

2017 ◽  
Vol 5 ◽  
pp. 135-146 ◽  
Author(s):  
Piotr Bojanowski ◽  
Edouard Grave ◽  
Armand Joulin ◽  
Tomas Mikolov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Training Data ◽  
Vector Representation ◽  
New Approach ◽  
Word Similarity ◽  
Art Performance ◽  
N Gram

Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.

scholarly journals Explore different methods to build Multi-Sense Embeddings

2020 ◽  
Author(s):  
Masashi Sugiyama
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Area ◽  
Word Embedding ◽  
The Other ◽  
Word Embeddings ◽  
Word Similarity ◽  
Better Than ◽  
Building Methods

In this last work, we did a exclusive survey related to multisense embeddings building methods. In this work, we extend our previous work and try to improve the current methods. Recently, word embeddings have been used in many natural language processing problems successfully and how to train a robust and accurate word embedding system efficiently is a popular research area. Since many, if not all, words have more than one sense, it is necessary to learn vectors for all senses of word separately. Therefore, in this project, we have explored two multi-sense word embedding models, including Multi-Sense Skip-gram (MSSG) model and Non-parametric Multi-sense Skip Gram model (NP-MSSG). Furthermore, we propose an extension of the Multi-Sense Skip-gram model called Incremental Multi-Sense Skip-gram (IMSSG) model which could learn the vectors of all senses per word incrementally. We evaluate all the systems on word similarity task and show that IMSSG is better than the other models.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

scholarly journals Word Embedding for Semantically Relative Words: an Experimental Study

2018 ◽  
Vol 25 (6) ◽  
pp. 726-733
Author(s):  
Maria S. Karyaeva ◽  
Pavel I. Braslavski ◽  
Valery A. Sokolov
Keyword(s):  
Experimental Study ◽  
Natural Language Processing ◽  
Language Processing ◽  
Intelligent Systems ◽  
Russian Language ◽  
Word Embedding ◽  
Semantic Relations ◽  
Automatic Extraction ◽  
Semantic Relationships ◽  
The Russian Language

The ability to identify semantic relations between words has made a word2vec model widely used in NLP tasks. The idea of word2vec is based on a simple rule that a higher similarity can be reached if two words have a similar context. Each word can be represented as a vector, so the closest coordinates of vectors can be interpreted as similar words. It allows to establish semantic relations (synonymy, relations of hypernymy and hyponymy and other semantic relations) by applying an automatic extraction. The extraction of semantic relations by hand is considered as a time-consuming and biased task, requiring a large amount of time and some help of experts. Unfortunately, the word2vec model provides an associative list of words which does not consist of relative words only. In this paper, we show some additional criteria that may be applicable to solve this problem. Observations and experiments with well-known characteristics, such as word frequency, a position in an associative list, might be useful for improving results for the task of extraction of semantic relations for the Russian language by using word embedding. In the experiments, the word2vec model trained on the Flibusta and pairs from Wiktionary are used as examples with semantic relationships. Semantically related words are applicable to thesauri, ontologies and intelligent systems for natural language processing.

Learning with Partial Supervision

2011 ◽  
pp. 1150-1157 ◽  
Author(s):  
Abdelhamid Bouchachia
Keyword(s):  
Supervised Learning ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Research Field ◽  
Unlabeled Data ◽  
Expression Recognition ◽  
Word Sense ◽  
Active Research ◽  
Text Chunking ◽  
Research Stream

Recently the field of machine learning, pattern recognition, and data mining has witnessed a new research stream that is <i>learning with partial supervisio</i>n -LPS- (known also as <i>semi-supervised learning</i>). This learning scheme is motivated by the fact that the process of acquiring the labeling information of data could be quite costly and sometimes prone to mislabeling. The general spectrum of learning from data is envisioned in Figure 1. As shown, in many situations, the data is neither perfectly nor completely labeled.<div><br></div><div>LPS aims at using available labeled samples in order to guide the process of building classification and clustering machineries and help boost their accuracy. Basically, LPS is a combination of two learning paradigms: supervised and unsupervised where the former deals exclusively with labeled data and the latter is concerned with unlabeled data. Hence, the following questions:</div><div><br></div><div><ul><li>Can we improve supervised learning with unlabeled data?&nbsp;<br></li><li>Can we guide unsupervised learning by incorporating few labeled samples?<br></li></ul></div><div><br></div><div>Typical LPS applications are medical diagnosis (Bouchachia &amp; Pedrycz, 2006a), facial expression recognition (Cohen et al., 2004), text classification (Nigam et al., 2000), protein classification (Weston et al., 2003), and several natural language processing applications such as word sense disambiguation (Niu et al., 2005), and text chunking (Ando &amp; Zhangz, 2005).</div><div><br></div><div>Because LPS is still a young but active research field, it lacks a survey outlining the existing approaches and research trends. In this chapter, we will take a step towards an overview. We will discuss (i) the background of LPS, (iii) the main focus of our LPS research and explain the underlying assumptions behind LPS, and (iv) future directions and challenges of LPS research. </div>

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