Input: What is natural language processing?
Natural language processing (NLP) is an area of research in which computers and people work together to exchange natural language text or speech. An important part of NLP is the ability of computers to understand and process human language, which is traditionally very complex for humans to master. Examples of NLP techniques include the automatic transcription of text and speech for transcriptionists, automatic detection, explanation, and annotation of health risks in written content, and the automatic categorization and tagging of financial content. Recent advances in natural language processing have created new ways of organizing and presenting information in human language, and these technologies will require continued study.
The most well-known type of NLP system is the semantic analysis NLP system, which uses rules to predict the correct entity type for every text or speech expression. Examples of well-established NLP types of systems are Word2Vec, which is used to produce the online dictionary. There are also well-known NLP experimentally-verified types of systems, such as LeTron, which is used to test whether automatic speech recognition systems are robust.
NLP also about the structure from the activity and meaning mapping NLP systems. For example, when reading text, semantic analysis NLP systems can infer the logical form (chapter heading) of every word, and the meaning of the whole passage depends on the analysis. Structure from analysis NLP systems also provide the analysis as tree, because the result of the whole process should be a sequence of sentences, similar to a relational table, or a sequence of words according to their meaning.
The progress in NLP is mainly driven by two principles, formal reasoning about the natural language and human language understanding (ELU). When formal meaning is understood, the meaning of any particular sentence can be precisely defined. When the meaning of any particular sentence can be precisely described in the form of a sequence of sentences, ELU method can be used. ELU is an analytical method, which works like a computer, letting the programmer control the algorithm.
This thesis gives an introduction to NLP, focusing on formal meaning and meaning-based algorithms, and introducing the concept of signal processing. The thesis discusses the ELU algorithm, called Watson, which won the IBM artificial intelligence competition by using natural language processing and ELU algorithm.
It gives an overview of the progress in natural language processing, and introduces the concepts of lexical analysis-production and formal semantics. It describes the workflow of a NLP process, which comprises analyzing a corpus of words into an parse tree, and then determining the semantics of the parse tree using first-order logic. It also discusses concepts such as meaning, acronym, person, sentence, event, and action, and the algorithms used to represent each piece of information.
In this thesis, the workflow of a NLP process is described, focusing on phrase-focused NLP. The process moves from the transcription of a speech or text to producing a text with added meaning from the captured speech or text. The text with meaning is then used for a conversational experience, or it can be used for triggering an action.
The process then takes this phrase-focused workflow and extends it to sentences-and-entities or sentences-and-captures workflow, using these techniques to generate dialog from capture to conversation.
It then extends the workflow to also produce transcripts in the third-person singular, instead of first-person plural, for a fully conversational workflow, and to capture feedback during the conversation.
It focuses on creating a truly conversational workflow, and implements each of the features as a standalone workflow that incorporates the other two together.
It also extends the workflow to also capture suggestions during the conversation, for both the first- and the third-party TTS engines.
With these additions, the workflow produces a fully conversational workflow that can capture feedback during the conversation, can be used to trigger a capture, and can capture in either the first- or the third-person.
The workflow also extends the capture process, capturing a conversation between a human interviewer and an employee, as well as capturing audio file of the entire capture.
Finally, it extends the conversation process to also capture audio file of the entire conversation, a third-party TTS engine, as well as a transcript of the entire conversation.
To produce a truly conversant TTS engine and TTS engine, this workflow uses the Google TTS engine, which has proven to be a highly accurate and reliable way to create authentic and believable American English voices.
This is the second iteration of the workflow, focusing on expanding the workflow to also capture the audio file of the entire conversation, a third-party TTS engine, as well as a transcript of the entire conversation.
This is the first iteration of the workflow, focused on capturing audio file of the entire conversation. The audio file was captured using Audacity, a free but free-to-use audio editor.
There are lots of different approaches to transcribing
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