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| 21 |
Effect of Lexical-Semantic Cues during Real-Time Sentence Processing in Aphasia
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In: Brain Sciences; Volume 12; Issue 3; Pages: 312 (2022)
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| 22 |
Lexical Category and Downstep in Japanese
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In: Languages; Volume 7; Issue 1; Pages: 25 (2022)
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| 23 |
A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis
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In: Mathematics; Volume 10; Issue 6; Pages: 914 (2022)
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| 24 |
Grammatical Gender Disambiguates Syntactically Similar Nouns
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In: Entropy; Volume 24; Issue 4; Pages: 520 (2022)
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| 25 |
The Spell-Out of Non-Heads in Spanish Compounds: A Nanosyntactic Approach
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In: Languages; Volume 7; Issue 2; Pages: 105 (2022)
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| 26 |
On the Nature of Syntactic Satiation
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In: Languages; Volume 7; Issue 1; Pages: 38 (2022)
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| 27 |
Why Is Inflectional Morphology Difficult to Borrow?—Distributing and Lexicalizing Plural Allomorphy in Pennsylvania Dutch
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In: Languages; Volume 7; Issue 2; Pages: 86 (2022)
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| 28 |
Semantic Feature Extraction Using SBERT for Dementia Detection
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In: Brain Sciences; Volume 12; Issue 2; Pages: 270 (2022)
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| 29 |
Root, Thematic Vowels and Inflectional Exponents in Verbs: A Morpho-Syntactic Analysis
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In: Languages; Volume 7; Issue 2; Pages: 104 (2022)
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| 30 |
Learning the Morphological and Syntactic Grammars for Named Entity Recognition
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In: Information; Volume 13; Issue 2; Pages: 49 (2022)
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Abstract:
In some languages, Named Entity Recognition (NER) is severely hindered by complex linguistic structures, such as inflection, that will confuse the data-driven models when perceiving the word’s actual meaning. This work tries to alleviate these problems by introducing a novel neural network based on morphological and syntactic grammars. The experiments were performed in four Nordic languages, which have many grammar rules. The model was named the NorG network (Nor: Nordic Languages, G: Grammar). In addition to learning from the text content, the NorG network also learns from the word writing form, the POS tag, and dependency. The proposed neural network consists of a bidirectional Long Short-Term Memory (Bi-LSTM) layer to capture word-level grammars, while a bidirectional Graph Attention (Bi-GAT) layer is used to capture sentence-level grammars. Experimental results from four languages show that the grammar-assisted network significantly improves the results against baselines. We also investigate how the NorG network works on each grammar component by some exploratory experiments.
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Keyword:
deep learning; language processing; morphology; named entity recognition; syntax
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URL: https://doi.org/10.3390/info13020049
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| 31 |
Gender Agreement in a Language Contact Situation
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In: Languages; Volume 7; Issue 2; Pages: 81 (2022)
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| 32 |
Preposition Stranding in Spanish–English Code-Switching
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In: Languages; Volume 7; Issue 1; Pages: 45 (2022)
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| 35 |
Syntactic deficits in language comprehension in individuals with schizophrenia and Broca's aphasia ...
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| 36 |
Syntactic deficits in language comprehension in individuals with schizophrenia and Broca's aphasia ...
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| 37 |
Supplementary materials for "Leveraging graph algorithms to speed up the annotation of large rhymed corpora" by Julien Baley, published in CLAO 51.1 (2022) ...
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| 38 |
Supplementary materials for "Leveraging graph algorithms to speed up the annotation of large rhymed corpora" by Julien Baley, published in CLAO 51.1 (2022) ...
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| 40 |
Supplementary material for: "Word order constraints on event-internal modifiers" ...
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