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Register a new userPredicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining
توقع التركيبات اللغوية الناشئة عبر الوقت: ملحق الإطار النحوي عبر تسلسل متعدد الوسائط
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تعتمد اللغة الطبيعية على معجم محدود للتعبير عن مجموعة غير محدودة من الأفكار الناشئة. هناك نتيجة واحدة لهذا التوتر هي تشكيل مؤلفات جديدة، بحيث يمكن دمج الوحدات اللغوية الحالية مع العناصر الناشئة في تعبيرات جديدة. نحن نطور إطارا يستغل الآليات المعرفية للسلاسل والمعرفة متعددة الوسائط للتنبؤ التعبيرات التركيبية الناشئة عبر الزمن. نقدم نموذج تمديد الإطار النحوي (SFEM) الذي يستمد على نظرية المدعون والمعرفة من الاهتمام "، والفهور"، واللغة "" لاستنتاج كيفية توسيع الأفعال إطاراتها لتشكيل مؤلفات جديدة مع الأسماء الحالية والرواية. نقيم SFEM بصرامة على 1) طرائق المعرفة و 2) تصنيف نماذج من التفصيل، في كوربوس الإنجليزية المحلينة على مدى 150 عاما الماضية. نظرا لأن SFEM Multimodal يتوقع بناء جملة الفعل والجدات التي ظهرت حديثا أفضل بكثير من النماذج المتنافسة باستخدام المعرفة اللغوية أو غير المستمرة البحتة. نجد دعما لوجهة نظر مثالية للسلاسل بدلا من عرض النموذج الأولي والكشف عن كيفية أن يكون النهج المشترك للسلسل متعدد الوسائط أمرا أساسيا لإنشاء استخدام اللغة الحرفية والجازرة بما في ذلك الاستعارة و Methymyy.
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.
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