Much of the progress in contemporary NLP has come from learning representations, such as masked language model (MLM) contextual embeddings, that turn challenging problems into simple classification tasks. But how do we quantify and explain this effec
t? We adapt general tools from computational learning theory to fit the specific characteristics of text datasets and present a method to evaluate the compatibility between representations and tasks. Even though many tasks can be easily solved with simple bag-of-words (BOW) representations, BOW does poorly on hard natural language inference tasks. For one such task we find that BOW cannot distinguish between real and randomized labelings, while pre-trained MLM representations show 72x greater distinction between real and random labelings than BOW. This method provides a calibrated, quantitative measure of the difficulty of a classification-based NLP task, enabling comparisons between representations without requiring empirical evaluations that may be sensitive to initializations and hyperparameters. The method provides a fresh perspective on the patterns in a dataset and the alignment of those patterns with specific labels.
Starting from an existing account of semantic classification and learning from interaction formulated in a Probabilistic Type Theory with Records, encompassing Bayesian inference and learning with a frequentist flavour, we observe some problems with
this account and provide an alternative account of classification learning that addresses the observed problems. The proposed account is also broadly Bayesian in nature but instead uses a linear transformation model for classification and learning.
We propose a probabilistic account of semantic inference and classification formulated in terms of probabilistic type theory with records, building on Cooper et. al. (2014) and Cooper et. al. (2015). We suggest probabilistic type theoretic formulatio
ns of Naive Bayes Classifiers and Bayesian Networks. A central element of these constructions is a type-theoretic version of a random variable. We illustrate this account with a simple language game combining probabilistic classification of perceptual input with probabilistic (semantic) inference.
Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker duri
ng their life. Still, it would be hard to argue that NLP models have reached human-level capacity. In this position paper, we argue that the reason for the current limitations is a focus on information content while ignoring language's social factors. We show that current NLP systems systematically break down when faced with interpreting the social factors of language. This limits applications to a subset of information-related tasks and prevents NLP from reaching human-level performance. At the same time, systems that incorporate even a minimum of social factors already show remarkable improvements. We formalize a taxonomy of seven social factors based on linguistic theory and exemplify current failures and emerging successes for each of them. We suggest that the NLP community address social factors to get closer to the goal of human-like language understanding.
The field of NLP has made substantial progress in building meaning representations. However, an important aspect of linguistic meaning, social meaning, has been largely overlooked. We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP. We also identify key challenges for this new line of research.
The input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with the embedding layer often constituting the majority
of model parameters. It is thus common to use a smaller vocabulary to lower memory requirements and construct more interpertable models. We propose a vocabulary selection method that views words as members of a team trying to maximize the model's performance. We apply power indices from cooperative game theory, including the Shapley value and Banzhaf index, that measure the relative importance of individual team members in accomplishing a joint task. We approximately compute these indices to identify the most influential words. Our empirical evaluation examines multiple NLP tasks, including sentence and document classification, question answering and textual entailment. We compare to baselines that select words based on frequency, TF-IDF and regression coefficients under L1 regularization, and show that this game-theoretic vocabulary selection outperforms all baseline on a range of different tasks and datasets.
يعرف القانون المدني بأنه مجموعة القواعد الموضوعية التي ينظم األحوال العينية
والشخصية والتزامات بين األفراد سواء تعلقت بثروتهم أو بأشخاصهم , وهو يمثل الشريعة
العامة لباقي القوانين التي تفرعت عنه، لكونه يشتمل على نظرية عامة لاللتزامات صالحة
للتطبيق
كلما كان هناك فراغ أو نقص في جانب من الجوانب القانونية التي تهم الفروع
المنبثقة عنه كالقانون التجاري والقانون االجتماعي والقانون العقاري.
ويتصدى القانون المدني لتنظيم كل من:
- االلتزامات الشخصية، وتعني الروابط القانونية الشخصية ذات الطابع المالي
- التزامات المالية ذات الطابع العيني التي تهدف أصال إلى إنشاء حق عيني
أصلي كااللتزامات الناقلة للملكية أو التي ترد على منافع األعيان كالكراء
الطويل األمد، أو حق تبعي كالضمان العيني من قبيل الرهون واالمتيازات.
و تقسم قواعد القانون المدني إلى قسمين:
- القسم األول يهم قانون االلتزامات والعقود المغربي خاص بااللتزامات والحقوق
الشخصية ويتفرع إلى كتابين، يخصص األول منه للنظرية العامة لاللتزام، أما
الكتاب الثاني فيشمل تطبيقات هذه النظرية
- القسم الثاني خاص بالقواعد المنظمة للحقوق األصلية والتبعية
The purpuse of the speech whatever its type is the inflence so the speaker tries so hard to
produse linguistic words that direct the reciever towards a specific action.The importance
of the argumintation theory lies in the depending on the speech t
echniques which the
sender use in the speech .And which make his speech acceptable to the reciever.The
argumentation theory started in the fields of Linguistics ,Logic ,Anthropology…etc and
different other sciences.It becomes a complete theory after Perlman's researches which the
scientists work on.All the analytists and researchers get benefit from in the communication
and connecting theory.Relying on the speaker's techniques in achieving a successful
connecting that leags to a real communication is the purpose of argumenation theory which
is a still growing.And which considers the speech act theory as a scientific background.
The theory - the opinion of a lot of thinkers contemporary - important theories
philosophical, social and which established to enter the era of modernity and beyond, may
be a cash German philosopher Kant cornerstone in Renaissance European accordin
g to this
theory, so try this search with a stamp philosophical and social that highlights the origin of
the emergence of these critical theory since the beginning the first to Renaissance, as he
tries to that keeps track of their development historical and philosophical and social
conditions, political and the surrounding, and what is the position of some of the
philosophers of these critical theory. However, the question, which can ask here, is a form
of the following: is the critical theory in turn, social, political and in the community, and
do have contributed to change the desired which seeks to in the community. The basis of
this question it should we have to trace the process of its development historical, and to
identify the most important views of the philosophers, which they told them.
التشريع انعكاس لفلسفة السلطة الحاكمة و سياستها , و تنفيذ لما تضعه من استراتيجيات و خطط لإدارة الدولة و تنظيم المجتمع.
