[PDF] Top 20 Learning Semantic Representations in a Bigram Language Model
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Learning Semantic Representations in a Bigram Language Model
... of semantic representations from ...a semantic component, other factors, ...isolating semantic structure from the ...modelling semantic dependencies within the bigram in terms of ... See full document
6
Learning Representations for Detecting Abusive Language
... abusive language, and whether such a represen- tation can be used in supervised methods for de- tecting abusive ...for learning rep- resentations (Stormfront and Reddit, further de- tailed in Section ... See full document
9
Attentive Tensor Product Learning
... ural language generation and related tasks. The model has a novel architecture motivated by insights derived from the use of Tensor Product Representations for encoding and pro- cessing symbolic ... See full document
8
Pre trained language model representations for language generation
... The model has access to the entire input surrounding the current target ...bi-directional model contains 353M parameters and the uni- directional model 190M ...The learning rate is linearly ... See full document
8
Learning Language Representations for Typology Prediction
... vector representations of languages (Tsvetkov et ...current language as an ...the language and improve LM ...these language vectors results in something that looks roughly like a phy- ... See full document
7
Low Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations
... of learning a precise matching between English and Spanish words, the CLCRs establishes a high-level semantic connection be- tween the source and the target ... See full document
12
What Do We Learn from Word Associations? Evaluating Machine Learning Algorithms for the Extraction of Contextual Word Meaning in Natural Language Processing
... Keywords: Machine Learning; Algorithms; Natural Language Processing, Deep Learning, Vector 29.. Space Models, Semantic Similarity, Distributional Semantics, Latent Semantic Analys[r] ... See full document
21
On the Limits of Learning to Actively Learn Semantic Representations
... multi-task learning across different rep- resentations (Stanovsky and Dagan, 2018; Hersh- covich et ...natural language (He et ...active learning, an iterative procedure for selecting unlabeled ex- ... See full document
11
Learning Semantic Textual Similarity with Structural Representations
... scoring model, ...for learning struc- tural relationships; and (v) using a classifier stack- ing approach, structural models can be easily com- bined and integrated into existing feature-based STS ... See full document
5
Language Models as Representations for Weakly Supervised NLP Tasks
... of model — 20 layers for the PL-MRF, 7 lay- ers for the I-HMM, and 1000 clusters for the Brown ...All language model representations sig- nificantly outperform the SCL model and the T ... See full document
10
Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity
... natural language sentences that describe the events a human be- ing calls to mind from the human brain activ- ity input data observed by fMRI via the above caption-generation ...simple model, a 3-layered ... See full document
8
Learning Cross lingual Distributed Logical Representations for Semantic Parsing
... multilingual semantic parsing. Such a model allows two types of input signals: single source SL-S INGLE and multi-source SL-M ULTI ...However, semantic parsing with cross-lingual fea- tures has not ... See full document
7
Weak semantic context helps phonetic learning in a model of infant language acquisition
... Recent work has investigated whether infants could overcome such distributional ambiguity by incorporating top-down information, in particular, the fact that phones appear within words. At six months, infants begin to ... See full document
11
Selecting Query Term Alternations for Web Search by Exploiting Query Contexts
... a bigram language model is used to determine the alteration of the head word that best fits the ...a bigram language model of the query to determine the appropriate alteration ... See full document
8
Learning Structured Natural Language Representations for Semantic Parsing
... 3.1 Generating Ungrounded Representations At this stage, utterances are mapped to interme- diate representations with a transition-based algo- rithm. In general, the transition system generates the ... See full document
12
Semantic representations for knowledge modelling of a Natural Language Interface to Databases using ontologies
... Natural Language Interfaces to Databases (NLIDB) that have been implemented, they do not guarantee to provide a correct response in 100% of the ...of semantic modelling the elements that integrate the ... See full document
15
A New Bigram-PLSA Language Model for Speech Recognition
... combining bigram model and Probabilistic Latent Semantic Analysis (PLSA) is introduced for language ...PLSA model. An EM-based parameter estimation technique for the proposed ... See full document
8
What a neural language model tells us about spatial relations
... what semantic knowledge about spatial relations is captured in representations of a generative neural language ...the language model is able to encode a distinction between functional ... See full document
11
Hidden Understanding Models of Natural Language
... 3.1 Semantic Language Model For tree structured meaning representations, individual nonterminal nodes determine particular abstract semantic concepts.. In the semantic language model, ea[r] ... See full document
8
Understanding Semantic Implicit Learning through distributional linguistic patterns: A computational perspective
... The results of the above simulations suggest that only representations based on the distri- butional patterns of words can model tasks of semantic implicit learning. We arrive at this co[r] ... See full document
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