86,830 research outputs found
Building WordNet for Afaan Oromoo
WordNet is a lexical database which has many relations to disambiguate the sense of words for natural languages. From the WordNet relations synonyms and hyponym has major role for natural language processing and artificial intelligence applications. In this paper, word embedding (Word2Vec) and lexico-syntactic pattern (LSP) are developed to extract automatically synonyms and hyponyms respectively. For this study, the word embedding is evaluated on two specialized domain algorithms such as a continuous bag of words and Skip Gram algorithms and show superior results. Applying word embedding (Word2Vec) algorithms for Afaan Oromo texts has been registered 80.09% and 85.04% for the continuous bag of words and Skip Gram respectively. According to the result achieved in this study, the skip-gram algorithm does a better job for frequent pairs of words than a continuous bag of words. But, a continuous bag of words algorithm is faster while skip-gram is slower. A lexical syntactic pattern with the combination of Word2Vec and without Word2Vec is also evaluated using information retrieval evaluation metrics such as precision, recall and F-measure to extract hyponym relation from Afaan Oromoo texts. The precision, recall and F-measure have been registered by lexical syntactic patterns without the combination of Word2Vec is 66.73%, 72%, and 69.26% respectively and with the combination of Word2Vec 81.14%, 80.8%, and 81.1% have been registered for precision, recall and F-measure respectively. There are factors that could affect the accuracy of results: 1) the style of writer of Afaan Oromoo i.e. they write a noun phrase with many adjective to express the noun for the reader; and, 2) it is possible that some instances of the LSP are missed due to misspellings and other typographical errors. Keywords: Afaan Oromoo WordNet, Word embedding, Lexico syntactic patterns, Extraction of WordNet relations. DOI: 10.7176/CEIS/11-3-01 Publication date:May 31st 202
Phrase embedding learning based on external and internal context with compositionality constraint
Different methods are proposed to learn phrase embedding, which can be mainly divided into two strands. The first strand is based on the distributional hypothesis to treat a phrase as one non-divisible unit and to learn phrase embedding based on its external context similar to learn word embedding. However, distributional methods cannot make use of the information embedded in component words and they also face data spareness problem. The second strand is based on the principle of compositionality to infer phrase embedding based on the embedding of its component words. Compositional methods would give erroneous result if a phrase is non-compositional. In this paper, we propose a hybrid method by a linear combination of the distributional component and the compositional component with an individualized phrase compositionality constraint. The phrase compositionality is automatically computed based on the distributional embedding of the phrase and its component words. Evaluation on five phrase level semantic tasks and experiments show that our proposed method has overall best performance. Most importantly, our method is more robust as it is less sensitive to datasets
Myers-Briggs Type Indicator Personality Model Classification in English Text using Convolutional Neural Network Method
Myers-Briggs Type Indicator (MBTI) is a personality model developed by Katharine Cooks Briggs and Isabel Briggs Myers in 1940. It displays a combination of preferences from four domains. Generally, test takers need to answer about 50 to 70 questions, and it is relatively expensive to know MBTI personality. The researcher developed a personality classification system using the Convolutional Neural Network (CNN) method and GloVe (Global Vectors for Word Representation) word embedding to solve this problem. The dataset used in this research consists of 8,675 data from the Kaggle site. The steps in this research are downloading the dataset from Kaggle, text preprocessing, GloVe weighting, classification using the CNN method, and evaluation using accuracy from the Confusion Matrix. Based on the tests carried out, using GloVe weighting can improve the model accuracy rather than random weighting. The best GloVe word dimensions depend on the metrics used to measure the model performance and the data of the classes contained in the dataset. From the CNN hyperparameter tuning test, the Adamax optimizer performs better and produces higher accuracy than the Adam optimizer. In addition, the CNN hyperparameter tuning increased model accuracy more significantly compared with the best GloVe word embedding dimensions
Chinese Character Decomposition for Neural MT with Multi-Word Expressions
Chinese character decomposition has been used as a feature to enhance Machine
Translation (MT) models, combining radicals into character and word level
models. Recent work has investigated ideograph or stroke level embedding.
However, questions remain about different decomposition levels of Chinese
character representations, radical and strokes, best suited for MT. To
investigate the impact of Chinese decomposition embedding in detail, i.e.,
radical, stroke, and intermediate levels, and how well these decompositions
represent the meaning of the original character sequences, we carry out
analysis with both automated and human evaluation of MT. Furthermore, we
investigate if the combination of decomposed Multiword Expressions (MWEs) can
enhance the model learning. MWE integration into MT has seen more than a decade
of exploration. However, decomposed MWEs has not previously been explored.Comment: Accepted to publish in NoDaLiDa202
Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings
Word embeddings have gained significant attention as learnable
representations of semantic relations between words, and have been shown to
improve upon the results of traditional word representations. However, little
effort has been devoted to using embeddings for the retrieval of entity
associations beyond pairwise relations. In this paper, we use popular embedding
methods to train vector representations of an entity-annotated news corpus, and
evaluate their performance for the task of predicting entity participation in
news events versus a traditional word cooccurrence network as a baseline. To
support queries for events with multiple participating entities, we test a
number of combination modes for the embedding vectors. While we find that even
the best combination modes for word embeddings do not quite reach the
performance of the full cooccurrence network, especially for rare entities, we
observe that different embedding methods model different types of relations,
thereby indicating the potential for ensemble methods.Comment: 4 pages; Accepted at SIGIR'1
Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
Recently, researchers started to pay attention to the detection of temporal
shifts in the meaning of words. However, most (if not all) of these approaches
restricted their efforts to uncovering change over time, thus neglecting other
valuable dimensions such as social or political variability. We propose an
approach for detecting semantic shifts between different viewpoints--broadly
defined as a set of texts that share a specific metadata feature, which can be
a time-period, but also a social entity such as a political party. For each
viewpoint, we learn a semantic space in which each word is represented as a low
dimensional neural embedded vector. The challenge is to compare the meaning of
a word in one space to its meaning in another space and measure the size of the
semantic shifts. We compare the effectiveness of a measure based on optimal
transformations between the two spaces with a measure based on the similarity
of the neighbors of the word in the respective spaces. Our experiments
demonstrate that the combination of these two performs best. We show that the
semantic shifts not only occur over time, but also along different viewpoints
in a short period of time. For evaluation, we demonstrate how this approach
captures meaningful semantic shifts and can help improve other tasks such as
the contrastive viewpoint summarization and ideology detection (measured as
classification accuracy) in political texts. We also show that the two laws of
semantic change which were empirically shown to hold for temporal shifts also
hold for shifts across viewpoints. These laws state that frequent words are
less likely to shift meaning while words with many senses are more likely to do
so.Comment: In Proceedings of the 26th ACM International on Conference on
Information and Knowledge Management (CIKM2017
Comparative Analysis of Word Embeddings for Capturing Word Similarities
Distributed language representation has become the most widely used technique
for language representation in various natural language processing tasks. Most
of the natural language processing models that are based on deep learning
techniques use already pre-trained distributed word representations, commonly
called word embeddings. Determining the most qualitative word embeddings is of
crucial importance for such models. However, selecting the appropriate word
embeddings is a perplexing task since the projected embedding space is not
intuitive to humans. In this paper, we explore different approaches for
creating distributed word representations. We perform an intrinsic evaluation
of several state-of-the-art word embedding methods. Their performance on
capturing word similarities is analysed with existing benchmark datasets for
word pairs similarities. The research in this paper conducts a correlation
analysis between ground truth word similarities and similarities obtained by
different word embedding methods.Comment: Part of the 6th International Conference on Natural Language
Processing (NATP 2020
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