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Found 13 papers, 3 papers with code
Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification
We present a new form of ensemble method–Devil’s Advocate, which uses a deliberately dissenting model to force other submodels within the ensemble to better collaborate.
Modeling Mathematical Notation Semantics in Academic Papers
Natural language models often fall short when understanding and generating mathematical notation.
Taxonomy and Analysis of Sensitive User Queries in Generative AI Search
Although there has been a growing interest among industries to integrate generative LLMs into their services, limited experiences and scarcity of resources acts as a barrier in launching and servicing large-scale LLM-based conversational services.
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations
Despite recent explosion of interests in in-context learning, the underlying mechanism and the precise impact of the quality of demonstrations remain elusive.
Ruminating Word Representations with Random Noise Masking
Through the re-training process, some of noises can be compensated and other noises can be utilized to learn better representations.
Human-Like Active Learning: Machines Simulating the Human Learning Process
In the end, we showed not only that we can make build better machine training framework through the human experiment result, but also empirically confirm the result of human experiment through imitated machine experiments; human-like active learning have crucial effect on learning performance.
Ruminating Word Representations with Random Noised Masker
Next, we gradually add random noises to the word representations and repeat the training process from scratch, but initialize with the noised word representations.
Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings
We propose a novel and simple method for semi-supervised text classification.
General Classification
Semi-Supervised Text Classification
+1
Expansional Retrofitting for Word Vector Enrichment
Retrofitting techniques, which inject external resources into word representations, have compensated the weakness of distributed representations in semantic and relational knowledge between words.
Psychological State in Text: A Limitation of Sentiment Analysis
Starting with the idea that sentiment analysis models should be able to predict not only positive or negative but also other psychological states of a person, we implement a sentiment analysis model to investigate the relationship between the model and emotional state.
Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons
The method consists of 3 steps as follows: (i) Expanding 1 or more dimension(s) on all the word vectors, filling with their representative value.
What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State
As the first step to model emotional state of a person, we build sentiment analysis models with existing deep neural network algorithms and compare the models with psychological measurements to enlighten the relationship.
Re-presenting a Story by Emotional Factors using Sentimental Analysis Method
The results of CES-D and PANAS show the relationship between emotion and memory retrieval as follows: depressed people have shown a tendency of representing a story more negatively, and they seemed less expressive.
