Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userUnderstanding the Semantic Space: How Word Meanings Dynamically Adapt in the Context of a Sentence
فهم الفضاء الدلالي: كيف تعاني كلمة تتكيف ديناميكيا في سياق الجملة
318
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
كيف يفهم الناس معنى كلمة صغيرة "عند استخدامها لوصف البعوض أو الكنيسة أو كوكب؟في حين أن البشر لديهم قدرة رائعة على تشكيل معاني من خلال الجمع بين المفاهيم القائمة، فإن نمذجة هذه العملية تحديا.تتناول هذه الورقة هذا التحدي من خلال Cerebra (تمثيلات المعنى المعنى المعني بالسياق في الدماغ) نموذج الشبكة العصبية.يميز Cerebra كيف يتكيف معاني الكلمات بشكل ديناميكي في سياق جملة من خلال الحكم المتحلل في جمهورية FMRI إلى الكلمات والكلمات في ميزات الدلالية المجدولة في الدماغ.يوضح أن الكلمات في سياقات مختلفة لها تمثيلات مختلفة والكلمة التي تعني التغييرات بطريقة ذات معنى إلى الموضوعات البشرية.يمكن أن تستخدم التمثيلات القائمة على سياق Cerebra لجعل تطبيقات NLP أكثر تشبه الإنسان.
How do people understand the meaning of the word small'' when used to describe a mosquito, a church, or a planet? While humans have a remarkable ability to form meanings by combining existing concepts, modeling this process is challenging. This paper addresses that challenge through CEREBRA (Context-dEpendent meaning REpresentations in the BRAin) neural network model. CEREBRA characterizes how word meanings dynamically adapt in the context of a sentence by decomposing sentence fMRI into words and words into embodied brain-based semantic features. It demonstrates that words in different contexts have different representations and the word meaning changes in a way that is meaningful to human subjects. CEREBRA's context-based representations can potentially be used to make NLP applications more human-like.
References used
https://aclanthology.org/
rate research
Read More
As hate speech spreads on social media and online communities, research continues to work on its automatic detection. Recently, recognition performance has been increasing thanks to advances in deep learning and the integration of user features. This
work investigates the effects that such features can have on a detection model. Unlike previous research, we show that simple performance comparison does not expose the full impact of including contextual- and user information. By leveraging explainability techniques, we show (1) that user features play a role in the model's decision and (2) how they affect the feature space learned by the model. Besides revealing that---and also illustrating why---user features are the reason for performance gains, we show how such techniques can be combined to better understand the model and to detect unintended bias.
Traditional synonym recommendations often include ill-suited suggestions for writer's specific contexts. We propose a simple approach for contextual synonym recommendation by combining existing human-curated thesauri, e.g. WordNet, with pre-trained l
anguage models. We evaluate our technique by curating a set of word-sentence pairs balanced across corpora and parts of speech, then annotating each word-sentence pair with the contextually appropriate set of synonyms. We found that basic language model approaches have higher precision. Approaches leveraging sentence context have higher recall. Overall, the latter contextual approach had the highest F-score.
Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously captu
re the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.
We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are eit
her mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three real-world MWP datasets demonstrate the superior performance of our approach compared to various baselines.
Lemmatization is often used with morphologically rich languages to address issues caused by morphological complexity, performed by grammar-based lemmatizers. We propose an alternative for this, in form of a tool that performs lemmatization in the spa
ce of word embeddings. Word embeddings as distributed representations natively encode some information about the relationship between base and inflected forms, and we show that it is possible to learn a transformation that approximately maps the embeddings of inflected forms to the embeddings of the corresponding lemmas. This facilitates an alternative processing pipeline that replaces traditional lemmatization with the lemmatizing transformation in downstream processing for any application. We demonstrate the method in the Finnish language, outperforming traditional lemmatizers in example task of document similarity comparison, but the approach is language independent and can be trained for new languages with mild requirements.
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
