Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPredicting and Explaining French Grammatical Gender
توقع وشرح الجنس النحوي الفرنسي
152
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
قد يتم تحديد الجنس النحاسي من قبل دلالات أو إجمالية أو علم الصوتيات أو يمكن أن يكون تعسفيا.تحديد الأنماط في العوامل التي تحكم نون الجنسين يمكن أن تكون مفيدة لمتعلمي اللغة، وفهم المصادر اللغوية الفطرية للتحيز بين الجنسين.قد يتم استبدال النهج اليدوية القائمة على القواعد اليدوية من خلال النهج الحسابية الأكثر دقة وقابلة للتطوير ولكن أصعب من أجل تفسيرها للتنبؤ بنوع الجنس من المعلومات النموذجية.في هذا العمل، نقترح نماذج تصنيف الجنسية القابلة للتفسير للفرنسية، والتي تحصل على أفضل ما في العالمين.نقدم نهج عصبي عالية الدقة التي تعززها نهج قائم على بديل عالمي جديد لتوضيح التنبؤات.نقدم سمات مساعدة "لتوفير تعقيد تفسير الضبط.
Grammatical gender may be determined by semantics, orthography, phonology, or could even be arbitrary. Identifying patterns in the factors that govern noun genders can be useful for language learners, and for understanding innate linguistic sources of gender bias. Traditional manual rule-based approaches may be substituted by more accurate and scalable but harder-to-interpret computational approaches for predicting gender from typological information. In this work, we propose interpretable gender classification models for French, which obtain the best of both worlds. We present high accuracy neural approaches which are augmented by a novel global surrogate based approach for explaining predictions. We introduce auxiliary attributes' to provide tunable explanation complexity.
References used
https://aclanthology.org/
rate research
Read More
Accurately dealing with any type of ambiguity is a major task in Natural Language Processing, with great advances recently reached due to the development of context dependent language models and the use of word or sentence embeddings. In this context
, our work aimed at determining how the popular language representation model BERT handle ambiguity of nouns in grammatical number and gender in different languages. We show that models trained on one specific language achieve better results for the disambiguation process than multilingual models. Also, ambiguity is generally better dealt with in grammatical number than it is in grammatical gender, reaching greater distance values from one to another in direct comparisons of individual senses. The overall results show also that the amount of data needed for training monolingual models as well as application should not be underestimated.
Natural language relies on a finite lexicon to express an unbounded set of emerging ideas. One result of this tension is the formation of new compositions, such that existing linguistic units can be combined with emerging items into novel expressions
. We develop a framework that exploits the cognitive mechanisms of chaining and multimodal knowledge to predict emergent compositional expressions through time. We present the syntactic frame extension model (SFEM) that draws on the theory of chaining and knowledge from percept'', concept'', and language'' to infer how verbs extend their frames to form new compositions with existing and novel nouns. We evaluate SFEM rigorously on the 1) modalities of knowledge and 2) categorization models of chaining, in a syntactically parsed English corpus over the past 150 years. We show that multimodal SFEM predicts newly emerged verb syntax and arguments substantially better than competing models using purely linguistic or unimodal knowledge. We find support for an exemplar view of chaining as opposed to a prototype view and reveal how the joint approach of multimodal chaining may be fundamental to the creation of literal and figurative language uses including metaphor and metonymy.
Since language is a natural concrete phenomenon, it became a fact that
language has been a matter of induction by making it go through
experiment in attempt to attain the rules that can take hold of the
language's partial phenomena and organize th
em in general regulations,
and if we observed the linguistic matter which grammarians investigated,
we could find that their work involved both the complete induction and
the incomplete induction according to Aristotle's induction method, but
they disagreed with this method in accordance with the nature of Islamic
method of thinking; therefore they had their own distinguishing method
of induction.
Understanding robustness and sensitivity of BERT models predicting Alzheimer's disease from text is important for both developing better classification models and for understanding their capabilities and limitations. In this paper, we analyze how a c
ontrolled amount of desired and undesired text alterations impacts performance of BERT. We show that BERT is robust to natural linguistic variations in text. On the other hand, we show that BERT is not sensitive to removing clinically important information from text.
The syntax is the spirit of language , the core of its movement, it is living heart.
And it is way of interpretation which is a pure mental manner . Taken by Arab Grammarians, so they gave it great effort in order to interpret the syntactic and morp
hological bases .
The following research considers the most important views of the old and modern syntax-specialists as starting point of studying the syntax – interpretations at two levels : the first Level contains concept and approach, It monitors the concept of reasoning in as, and the implications at rules of this term, the statement its motive which is based language in use . and the second level is referential . It demands its elements and the views of Arab syntax in it , and it clears its rules which was established tell the six migration century .
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
