2015/08/10 by Jianpeng Cheng, Cheng, Jianpeng, Dimitri Kartsaklis +1
Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Neural and Evolutionary Computing (cs.NE) #Topic Modeling #cs.AI #cs.CL #cs.NE
paper · pdf · doi:10.48550/arxiv.1508.02354
Accepted for presentation at EMNLP 2015
openalex publication_date 2015/08/10 · arxiv created 2015/08/13 · arxiv updated 2015/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.