Semantic Relatedness for All (Languages): A Comparative Analysis of Multilingual Semantic Relatedness Using Machine Translation

Freitas, André; Barzegar, Siamak; Sales, Juliano Efson; Handschuh, Siegfried; Davis, Brian

doi:10.1007/978-3-319-49004-5_14

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…The constructed paradigmatic and syntagmatic embeddings successfully dissociates two semantic principles as shown by the word-pair semantic proximity ranking task (Table II), measured with paradigmatic-/syntagmatic-specific benchmarks. The French benchmark data are translated [44] from Out of Vocabulary .002 .024…”

Section: A Twofold Dissociation In Embeddingsmentioning

confidence: 99%

Functional MRI Investigation on Paradigmatic and Syntagmatic Lexical Semantic Processing

Ying

Ploux

2020

Preprint

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The word embeddings related to paradigmatic and syntagmatic axes are applied in an fMRI encoding experiment to explore human brain's activity pattern during story listening. This study proposes the construction of paradigmatic and syntagmatic semantic embeddings respectively by transforming WordNet-alike knowledge bases and subtracting paradigmatic information from a statistical word embedding. It evaluates the semantic embeddings by leveraging word-pair proximity ranking tasks and contrasts voxel encoding models trained with the two types of semantic features to reveal the brain's spatial pattern for semantic processing. Results indicate that in listening comprehension, paradigmatic and syntagmatic semantic operations both recruit inferior (ITG) and middle temporal gyri (MTG), angular gyrus, superior parietal lobule (SPL), inferior frontal gyrus. A non-continuous voxel line is found in MTG with a predominance of paradigmatic processing. The ITG, middle occipital gyrus and the surrounding primary and associative visual areas are more engaged by syntagmatic processing. The comparison of two semantic axes' brain map does not suggest a neuroanatomical segregation for paradigmatic and syntagmatic processing. The complex yet regular contrast pattern starting from temporal pole, along MTG to SPL necessitates further investigation.

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Section: A Twofold Dissociation In Embeddingsmentioning

confidence: 99%

Functional MRI Investigation on Paradigmatic and Syntagmatic Lexical Semantic Processing

Ying

Ploux

2020

Preprint

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“…The next embedding evaluation we consider is a word similarity task. The ground truth data consists of pairs of words and a human-annotated similarity score averaged across all human evaluations from a translation of the WS-353 dataset (Freitas et al, 2016). The scores are computed via the cosine similarity between the vector representation of each word in a pair.…”

Section: Word Similaritymentioning

confidence: 99%

Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings

El-Kishky

Fu²,

Addawood

et al. 2019

Proceedings of the Fourth Arabic Natural Language Processing Workshop

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In this paper, we tackle the problem of "root extraction" from words in the Semitic language family. A challenge in applying natural language processing techniques to these languages is the data sparsity problem that arises from their rich internal morphology, where the substructure is inherently non-concatenative and morphemes are interdigitated in word formation. While previous automated methods have relied on human-curated rules or multiclass classification, they have not fully leveraged the various combinations of regular, sequential concatenative morphology within the words and the internal interleaving within templatic stems of roots and patterns. To address this, we propose a constrained sequence-tosequence root extraction method. Experimental results show our constrained model outperforms a variety of methods at root extraction. Furthermore, by enriching word embeddings with resulting decompositions, we show improved results on word analogy, word similarity, and language modeling tasks.

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“…The method is the sum of the vectors of multi-word expressions. To compute relatedness between vectors, we use the Indra implementation (Freitas et al, 2016) of the cosine similarity metric. The system computes cosine similarity for all possible pairwise combinations of tokens in each message.…”

Section: Computing Semantic Relatednessmentioning

confidence: 99%

Proceedings of the First Workshop on Economics and Natural Language Processing

2018

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This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) setup and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) setup. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental setups .

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Semantic Relatedness for All (Languages): A Comparative Analysis of Multilingual Semantic Relatedness Using Machine Translation

Cited by 11 publications

References 14 publications

Functional MRI Investigation on Paradigmatic and Syntagmatic Lexical Semantic Processing

Functional MRI Investigation on Paradigmatic and Syntagmatic Lexical Semantic Processing

Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings

Proceedings of the First Workshop on Economics and Natural Language Processing

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