What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
Her scientific interests lie mostly in Artificial intelligence, Natural language processing, Sentiment analysis, Support vector machine and Machine learning.
Her research integrates issues of Social relation, Situational ethics and Cognitive psychology in her study of Artificial intelligence.
Her studies deal with areas such as Bigram and Word as well as Natural language processing.
Her Sentiment analysis study incorporates themes from Subjectivity and Automatic summarization.
Her Subjectivity study combines topics from a wide range of disciplines, such as Popularity, Information extraction and Object.
In her research, Naive Bayes classifier, Principle of maximum entropy, Class, Similarity and Semantic similarity is intimately related to Categorization, which falls under the overarching field of Support vector machine.
Her most cited work include:
- Opinion Mining and Sentiment Analysis (5838 citations)
- Thumbs up? Sentiment Classification using Machine Learning Techniques (5728 citations)
- A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts (2717 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Artificial intelligence, Natural language processing, Parsing, Information retrieval and Language model.
Her research on Artificial intelligence frequently links to adjacent areas such as Machine learning.
Her Natural language processing research incorporates elements of Bigram, Word, Speech recognition and Similarity.
Her Information retrieval research is multidisciplinary, incorporating perspectives in Hyperlink and Centrality.
Her research in Sentiment analysis intersects with topics in Subjectivity, Support vector machine and Automatic summarization.
Her study focuses on the intersection of Support vector machine and fields such as Categorization with connections in the field of Naive Bayes classifier and Principle of maximum entropy.
She most often published in these fields:
- Artificial intelligence (45.93%)
- Natural language processing (34.81%)
- Parsing (13.33%)
What were the highlights of her more recent work (between 2017-2021)?
- Artificial intelligence (45.93%)
- Natural language processing (34.81%)
- Parsing (13.33%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Artificial intelligence, Natural language processing, Parsing, Dependency and Annotation.
Lillian Lee performs multidisciplinary study in Artificial intelligence and Structure in her work.
The various areas that Lillian Lee examines in her Natural language processing study include Concreteness, Image and Table.
Lillian Lee has researched Parsing in several fields, including F1 score, Web page and Machine translation.
Her Dependency study combines topics in areas such as Theoretical computer science and Projective test.
The concepts of her Annotation study are interwoven with issues in Sentence, Test and Multi-image.
Between 2017 and 2021, her most popular works were:
- Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (16 citations)
- Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents (11 citations)
- Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Features. (11 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Her primary areas of study are Natural language processing, Artificial intelligence, Cognitive psychology, Dependency grammar and Concreteness.
Her Natural language processing research is mostly focused on the topic Sentence.
Her Cognitive psychology study frequently links to related topics such as Conversation.
Her Dependency grammar research includes elements of Valency, Tree-adjoining grammar, Treebank, Projective test and Syntax.
Her Concreteness study frequently links to adjacent areas such as Image.
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