Sökning: "Avsiktsklassificering"
Hittade 5 uppsatser innehållade ordet Avsiktsklassificering.
1. Recommendation of Text Properties for Short Texts with the Use of Machine Learning : A Comparative Study of State-of-the-Art Techniques Including BERT and GPT-2
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Text mining has gained considerable attention due to the extensive usage ofelectronic documents. The significant increase in electronic document usagehas created a necessity to process and analyze them effectively. LÄS MER
2. QPLaBSE: Quantized and Pruned Language-Agnostic BERT Sentence Embedding Model : Production-ready compression for multilingual transformers
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Transformer models perform well on Natural Language Processing and Natural Language Understanding tasks. Training and fine-tuning of these models consume a large amount of data and computing resources. Fast inference also requires high-end hardware for user-facing products. LÄS MER
3. 'Sorry, I didn't understand that' : A comparison of methods for intent classification for social robotics applications
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : An important feature in a social robot is the ability to understand natural language. One of the core components in a typical system for natural language understanding (NLU) is so called intent classification; the action of classifying user utterances based on the underlying intents of the user. LÄS MER
4. Intent classification through conversational interfaces : Classification within a small domain
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Natural language processing and Machine learning are subjects undergoing intense study nowadays. These fields are continually spreading, and are more interrelated than ever before. A case in point is text classification which is an instance of Machine learning(ML) application in Natural Language processing(NLP). LÄS MER
5. The Effect of Data Quantity on Dialog System Input Classification Models
M1-uppsats, KTH/Hälsoinformatik och logistikSammanfattning : This paper researches how different amounts of data affect different word vector models for classification of dialog system user input. A hypothesis is tested that there is a data threshold for dense vector models to reach the state-of-the-art performance that have been shown with recent research, and that character-level n-gram word-vector classifiers are especially suited for Swedish classifiers–because of compounding and the character-level n-gram model ability to vectorize out-of-vocabulary words. LÄS MER