Sökning: "Word Vector Models"

Visar resultat 16 - 20 av 21 uppsatser innehållade orden Word Vector Models.

1. 16. The Effect of Data Quantity on Dialog System Input Classification Models

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Johan Lipecki; Viggo Lundén; [2018]
   Nyckelord :Chatbot; Chatterbot; Virtual Assistant; Dialog System; Natural Language Understanding; Word Embedding; Word Vector Models; Text Classification; Chattbot; Virtuell Assistent; Dialogsystem; Naturlig språkbehandling; Ordinbäddning; Ordvektormodeller; Textklassificering;

   Sammanfattning : 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

3. 17. Topic discovery and document similarity via pre-trained word embeddings

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Simin Chen; [2018]
   Nyckelord :Document similarity; document representation; word embedding; natural language processing; topic modeling; ;

   Sammanfattning : Throughout the history, humans continue to generate an ever-growing volume of documents about a wide range of topics. We now rely on computer programs to automatically process these vast collections of documents in various applications. Many applications require a quantitative measure of the document similarity. LÄS MER

4. 18. Smirking or Smiling Smileys? : Evaluating the Use of Emoticons to Determine Sentimental Mood

   Kandidat-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)

   Författare :Elias Lousseief; Tobias Hindersson; [2015]
   Nyckelord :;

   Sammanfattning : Machine Learning classifiers are commonly used for the purpose of Sentiment Analysis. These classifiers use annotated training data from which they learn to predict the sentiment of texts, for example whether a text conveys a positive or a negative sentiment. LÄS MER

5. 19. Automatic Lexicon Extraction on RandomIndexing Word Spaces using Small Seed Lexica

   Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)

   Författare :AMARU CUBA GYLLENSTEN; [2014]
   Nyckelord :;

   Sammanfattning : Automatic bilingual lexicon extraction has many applications in Natural Language Processing, but often times requires highly structured,parallel, data or extensive bilingual seed lexicas to get reasonably good performance. Random Indexing models with a small bilingual seed lexicon could be used to perform (semi-)automatic lexicon extraction using only separate monolingual data. LÄS MER

7. 20. Word Space Models for Web User Clustering and Page Prefetching

   Kandidat-uppsats, Institutionen för datavetenskap; Filosofiska fakulteten

   Författare :Albin Sundin; [2012]
   Nyckelord :LSA; RI; CAS-C; Clustering; Prefetching; Web mining; Segmentering;

   Sammanfattning : This study evaluates methods for clustering web users via vector space models, for the purpose of web page prefetching for possible applications of server optimization. An experiment using Latent Semantic Analysis (LSA) is deployed to investigate whether LSA can reproduce the encouraging results obtained from previous research with Random Indexing (RI) and a chaos based optimization algorithm (CAS-C). LÄS MER