Random Language Model
نویسندگان
چکیده
منابع مشابه
Combination of random indexing based language model and n-gram language model for speech recognition
This paper presents the results and conclusion of a study on the introduction of semantic information through the Random Indexing paradigm in statistical language models used in speech recognition. Random Indexing is an alternative to Latent Semantic Analysis (LSA) that addresses the scalability problem of LSA. After a brief presentation of Random Indexing (RI), this paper describes, different ...
متن کاملUsing Random Forests in the Structured Language Model
In this paper, we explore the use of Random Forests (RFs) in the structured language model (SLM), which uses rich syntactic information in predicting the next word based on words already seen. The goal in this work is to construct RFs by randomly growing Decision Trees (DTs) using syntactic information and investigate the performance of the SLM modeled by the RFs in automatic speech recognition...
متن کاملLanguage Model-Based Document Clustering Using Random Walks
We propose a new document vector representation specifically designed for the document clustering task. Instead of the traditional termbased vectors, a document is represented as an -dimensional vector, where is the number of documents in the cluster. The value at each dimension of the vector is closely related to the generation probability based on the language model of the corresponding docum...
متن کاملSpatial Beta Regression Model with Random Effect
Abstract: In many applications we have to encountered with bounded dependent variables. Beta regression model can be used to deal with these kinds of response variables. In this paper we aim to study spatially correlated responses in the unit interval. Initially we introduce spatial beta generalized linear mixed model in which the spatial correlation is captured through a random effect. T...
متن کاملRandom Forests In Language Modeling
In this paper, we explore the use of Random Forests (RFs) (Amit and Geman, 1997; Breiman, 2001) in language modeling, the problem of predicting the next word based on words already seen before. The goal in this work is to develop a new language modeling approach based on randomly grown Decision Trees (DTs) and apply it to automatic speech recognition. We study our RF approach in the context of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review Letters
سال: 2019
ISSN: 0031-9007,1079-7114
DOI: 10.1103/physrevlett.122.128301