In our second trial, Minnesota French Dialogue Corpus 18 was used; the main aim of using this corpus is to apply the same machine learning approach used with English DDC on other languages. We found out that we managed to build different chatbots speaking different languages based on the same machine learning approach, where only the text pre-processing was changed to meet up a particular new corpus annotation format.
After that, we aimed to build a chatbot for a language that suffers from the lack of NLP tools or has little processing technology. For this reason we used the corpus of Spoken Afrikaans Our Java program was extended to build default AIML categories in addition to atomic AIML files, so in case atomic matching failed, the default categories will be used, by this we elaborate the possibility of having more matches and gaining user satisfaction.
Two machine learning approached were adapted to build default categories: first word approach and most significant word. For each atomic pattern, we generated a default one that holds the first word followed by a wild card to match any text, and then we associated it with the same template.
However, this approach was not enough to satisfy Afrikaans users. A frequency list was built out of the corpus, then for each atomic pattern, the least frequent word most significant was obtained based on the frequency list to generate a default pattern. Four categories holding most significant word in the first, middle, last positions, or alone were added to the default categories.
The feedback showed improvement in user satisfaction 2. Our next concern was to prove that the Java program we developed is capable of building millions of categories using huge corpora with more than one speaker and involving many domains.
For this purpose, the British National Corpus 13 was selected. The BNC is a collection of text samples having more than million words extracted from modern British English texts of all kinds, both spoken and written. The software was revised to handle the BNC format, and the BNC lemmatized frequency list was used to extract least frequent word.
To handle the problem of having a very large AIML file, different chatbot prototypes were built that talk in multi domains: sport, word affairs, travel, media, and food, and represent variety of teenagers speech: Michael, Peter, Robin, loudmouth, etc. As a result, we managed to automatically generate the largest AIML model ever 1,, categories and to apply the generated chatbots to illustrate the type of English used within a specific domain or speaker-type 5 - 6.
Can we build a useful chatbot from a monologue corpus where no turns are found? To answer this question we used the holy book of Islam Qur'an. The Qur'an consists of soora sections where each soora consists of more than one ayya verse. Those sooras are grouped in 30 parts chapters written in the Arabic Language as delivered to Prophet Mohammad.
Muslims used the Qur'an to direct them in every aspect of their life, and they need to memorize it and read it in their prayers. In order to handle non-conversational structure of Qur'an, each ayya is considered as a pattern and the successor one, as a template which could be a useful tool in learning the Qur'an. Two chatbots were created: Arabic Qur'anic chatbot that accepts Arabic input and responds with the Arabic verses; the second one is the Arabic-English chatbot that accepts user input in English and responds with both Arabic-English verse s.
This version could be useful for English speakers who want to learn the Qur'an 3 - 4. From the previous corpora used, we found out that machine learning approach works best when the user's conversation with the chatbot is likely to be constrained to a specific topic, and this topic is comprehensively covered in the training corpus. In this updated version, a question represents a pattern, and the answer represents the template in building atomic AIML files.
The frequency list was constructed from questions patterns. Different categories are added to extend the chance of finding answers, where the answer is either a set of links in case most significant words are found in more than one question or a direct answer in the instance where only one match was found.
In addition to first word and most significant word 1st , we extracted second most significant one 2nd least frequent words. For each significant word, four default categories were added to handle a different position of the word in a pattern; another category holding first word, 1st or 2nd most significant word as appeared in the original question was generated.
Since there is no specific format to ask the question, there are cases where some users could find answers while others could not. The great success with using chatbot as a tool to answer SoC FAQs encouraged us to try other FAQs, or Questions Answers QA corpora to investigate the possibility of using a chatbot as a tool to access an information portal without the need for sophisticated natural language processing or logical inference.
Overall User trials with AskJeeves, Google, and generated chatbot demonstrate that chatbot is a viable alternative, and in fact many users prefer it to Google as a tool to access FAQ databases 8 - We have observed that there is no need for sophisticated natural language analysis or logical inference; a simple but large set of pattern-template matching rules is sufficient.
During our journey with chatbots, especially with ALICE, and the developed techniques to retraining them with different corpora, we found out that a chatbot could be used for different purposed not restricted to entertainment issues. Nowadays, a lot of chatbots were generated and used for other purposes; some of them are listed in Table 2. Table 2 Some Chatbots and its Usages. This paper overviewed ALICE chatbot in terms of the knowledge base and its pattern matching technique.
We managed to build a software program that reads from a corpus and converts it to the ALICE knowledge base. Different corpora were used to retrain ALICE which reveals other useful applications of a chatbot rather than an entertainment tool. We managed to demonstrate that a simple ALICE-style chatbot engine could produce results at least as well-appreciated as those from the most popular commercial web search engine.
We did not need a sophisticated natural language analysis or logical inference; a simple but large set of pattern-template matching rules was sufficient. Abu Shawar, B. Using dialogue corpora to retrain a chatbot system. In Archer, D. Using the Corpus of Spoken Afrikaans to generate an Afrikaans chatbot. DOI: Accessing an Information system by chatting.
Metais Eds. Berlin: Springer-Verlag. A chatbot system as a tool to animate a corpus. For every sort of question, a remarkable pattern must be accessible in the database to give a reasonable response. With a number of pattern combinations, it makes a hierarchical structure.
We utilize algorithms to lessen the classifiers and produce the more reasonable structure. For example, it may be a payment system in your E-commerce chatbot.
So, the phases during the conversation of chat are separately stored. With context, you can easily relate expectations with the necessity of comprehending the last question. Expectations: This is what a chatbot must fulfill when the customer says sends an inquiry. Which can be the same for different inquiries. Hence, all user typing text show a single command which is the identifying tag; white shoes. Which is then utilized to choose a relevant answer. Natural Language Processing includes the following steps;.
For many applications, the chatbot is connected to the database. The database is utilized to sustain the chatbot and provide appropriate responses to every user. NLP can translate human language into data information with a blend of text and patterns that can be useful to discover applicable responses. There are NLP applications, programming interfaces, and services that are utilized to develop chatbots.
And make it possible for all sort of businesses — small, medium or large-scale industries. The primary point here is that smart bots can help increase the customer base by enhancing the customer support services, thereby helping to increase sales.
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Grey out: Paid chatbots Expired chatbots Protected chatbots. Chatbot info What is a chatbot? Add your chatbot s! Virtual agent. The Chat Bot Future A chat bot is a humanlike conversational character. Its conversational skills and other humanlike behaviour is simulated through artificial intelligence.
It often acts as a virtual assistant and it can have its own visualisation through an avatar or it is faceless. We expect that through the years every conversational chat bot will grow into a real virtual human. Cool chatbot animation.
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Text recognition , Avatar Application:. Proof of Concept Synonym used:. Tom Walker Editor, Chatbots. Chatbot Statistics Directory: 1, chatbots Companies: developers Community: 25, members Synonyms: synonyms.
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