This thesis explored using construction grammar and ontologies in conversational systems. The author built two early experimental systems using these techniques. Construction grammar represents language as constructions pairing form and meaning. Ontologies allow for more explicit semantics compared to databases. The author developed a stemmer called UEA-Lite and a system called KIA that incorporated construction grammar, ontologies, and machine learning to understand and respond to natural language.
We all do our research and put an effort in making a clear and an accurate presentation, but I'd be glad if this could help especially for those who are taking major in English and the like. Good luck!
A proper credit would be appreciated.
• Jay-ar A. Padernal, BSEd Major in English, University of Mindanao
A COMPUTATIONAL APPROACH FOR ANALYZING INTER-SENTENTIAL ANAPHORIC PRONOUNS IN...ijnlc
This paper presents a strategy and a computational model for solving inter-sentential anaphoric pronouns
in Vietnamese paragraphs composing simple sentences. The strategy is proposed based on grammatical
features of nouns and the focus phenomenon when using pronouns in Vietnamese. In this research, we
consider only nouns and pronouns which are human objects in the paragraph, and each anaphoric
pronoun will appear one time in one sentence and can appear in adjacent sentences. The computational
model is implemented in Prolog and based on applying and improving the models of Mark Johnson and
Ewan Klein, had been improved by Covington and Schmitz, with theoretical background of Discourse
Representation Theory.Analysis of test results shows that this approach which based on linguistic theories
helps for well solving inter-sentential anaphoric pronouns in Vietnamese paragraphs.
Our starting point in this paper is that the traditional approach used for the teaching of Chinese measure words is misleading and has failed to achieve the goals of teaching, since the mastering of Chinese measure words is felt to be a difficult aspect of Chinese grammar for both teachers and students. This sense of difficulty is due to a variety of factors.
First, members within a given category of a Chinese measure word seem to have little or no connection among them, since for most categories there are almost no explanations for their semantic motivation. Second, we too often forget that this Chinese system of linguistic classification is a flexible one, and poses many questions to the traditional view, which promotes the idea of one-to-one concordance, i.e. each noun has its own classifier. And last, but not least, when carrying out a contrastive study, we often conclude that measure words cannot be translated, but it does not matter, since “they bear no meaning”.
Following the work of Tai & Wang (1990) on a study of the classifier tiao, an increasing number of papers concerning the subject of Chinese measure words and human categorization have been published. This recent research is based on the principles of cognitive linguistics and concludes that Chinese measure words are not an arbitrary linguistic device, but represent an interesting type of human categorization which needs further study. We believe that the new approach provided by cognitive linguistics is optimal for solving most problems and difficulties concerning the teaching and learning of Chinese measure words., i.e. consistency of categories, concordance, classification, translation, their discursive and pragmatic role, etc.
This paper presents a set of linguistically informed and motivated multilingual alignments -- the CLUE4Translation Alignments -- covering several categories of multiwords and phrasal units, which constitute important challenges to high quality machine translation. The alignments comprise all possible word combinations between English, French, Portuguese, and Spanish parallel texts of the common test set of the Europarl corpus. The gold collection of the manually annotated alignments -- the Gold-CLUE-Translation -- is constituted of 400 sentences aligned according to previously proposed guidelines -- CLUE4Translation Alignment Guidelines -- for each language pair, resulting in a set of 2,400 alignments. The alignments were performed with the support of a new alignment tool -- CLUE-Aligner -- developed to facilitate the alignment of the translation units in the bitexts, including the alignment of non-contiguous multiwords and phrasal translation units. The Gold CLUE4Translation, the CLUE-Aligner, and the CLUE4Translation Alignment Guidelines are publicly available.
We all do our research and put an effort in making a clear and an accurate presentation, but I'd be glad if this could help especially for those who are taking major in English and the like. Good luck!
A proper credit would be appreciated.
• Jay-ar A. Padernal, BSEd Major in English, University of Mindanao
A COMPUTATIONAL APPROACH FOR ANALYZING INTER-SENTENTIAL ANAPHORIC PRONOUNS IN...ijnlc
This paper presents a strategy and a computational model for solving inter-sentential anaphoric pronouns
in Vietnamese paragraphs composing simple sentences. The strategy is proposed based on grammatical
features of nouns and the focus phenomenon when using pronouns in Vietnamese. In this research, we
consider only nouns and pronouns which are human objects in the paragraph, and each anaphoric
pronoun will appear one time in one sentence and can appear in adjacent sentences. The computational
model is implemented in Prolog and based on applying and improving the models of Mark Johnson and
Ewan Klein, had been improved by Covington and Schmitz, with theoretical background of Discourse
Representation Theory.Analysis of test results shows that this approach which based on linguistic theories
helps for well solving inter-sentential anaphoric pronouns in Vietnamese paragraphs.
Our starting point in this paper is that the traditional approach used for the teaching of Chinese measure words is misleading and has failed to achieve the goals of teaching, since the mastering of Chinese measure words is felt to be a difficult aspect of Chinese grammar for both teachers and students. This sense of difficulty is due to a variety of factors.
First, members within a given category of a Chinese measure word seem to have little or no connection among them, since for most categories there are almost no explanations for their semantic motivation. Second, we too often forget that this Chinese system of linguistic classification is a flexible one, and poses many questions to the traditional view, which promotes the idea of one-to-one concordance, i.e. each noun has its own classifier. And last, but not least, when carrying out a contrastive study, we often conclude that measure words cannot be translated, but it does not matter, since “they bear no meaning”.
Following the work of Tai & Wang (1990) on a study of the classifier tiao, an increasing number of papers concerning the subject of Chinese measure words and human categorization have been published. This recent research is based on the principles of cognitive linguistics and concludes that Chinese measure words are not an arbitrary linguistic device, but represent an interesting type of human categorization which needs further study. We believe that the new approach provided by cognitive linguistics is optimal for solving most problems and difficulties concerning the teaching and learning of Chinese measure words., i.e. consistency of categories, concordance, classification, translation, their discursive and pragmatic role, etc.
This paper presents a set of linguistically informed and motivated multilingual alignments -- the CLUE4Translation Alignments -- covering several categories of multiwords and phrasal units, which constitute important challenges to high quality machine translation. The alignments comprise all possible word combinations between English, French, Portuguese, and Spanish parallel texts of the common test set of the Europarl corpus. The gold collection of the manually annotated alignments -- the Gold-CLUE-Translation -- is constituted of 400 sentences aligned according to previously proposed guidelines -- CLUE4Translation Alignment Guidelines -- for each language pair, resulting in a set of 2,400 alignments. The alignments were performed with the support of a new alignment tool -- CLUE-Aligner -- developed to facilitate the alignment of the translation units in the bitexts, including the alignment of non-contiguous multiwords and phrasal translation units. The Gold CLUE4Translation, the CLUE-Aligner, and the CLUE4Translation Alignment Guidelines are publicly available.
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Abstract:
We are living in an age of rapidly advancing technology. History may view this period as one in which generative artificial intelligence is seen as reshaping the landscape and narrative of many technology-based fields of research and application. Times of disruptions often present both opportunities and challenges. We will discuss some areas that may be ripe for consideration in the field of Semantic Web research and semantically-enabled applications. Semantic Web research has historically focused on representation and reasoning and enabling interoperability of data and vocabularies. At the core are ontologies along with ontology-enabled (or ontology-compatible) knowledge stores such as knowledge graphs. Ontologies are often manually constructed using a process that (1) identifies existing best practice ontologies (and vocabularies) and (2) generates a plan for how to leverage these ontologies by aligning and augmenting them as needed to address requirements. While semi-automated techniques may help, there is typically a significant portion of the work that is often best done by humans with domain and ontology expertise. This is an opportune time to rethink how the field generates, evolves, maintains, and evaluates ontologies. We consider how hybrid approaches, i.e., those that leverage generative AI components along with more traditional knowledge representation and reasoning approaches to create improved processes. The effort to build a robust ontology that meets a use case can be large. Ontologies are not static however and they need to evolve along with knowledge evolution and expanded usage. There is potential for hybrid approaches to help identify gaps in ontologies and/or refine content. Further, ontologies need to be documented with term definitions and their provenance. Opportunities exist to consider semi-automated techniques for some types of documentation, provenance, and decision rationale capture for annotating ontologies. The area of human-AI collaboration for population and verification presents a wide range of areas of research collaboration and impact. Ontologies need to be populated with class and relationship content. Knowledge graphs and other knowledge stores need to be populated with instance data in order to be used for question answering and reasoning. Population of large knowledge graphs can be time consuming. Generative AI holds the promise to create candidate knowledge graphs that are compatible with the ontology schema. The knowledge graph should contain provenance information identifying how the content was populated and its source and correctness and currency should be checked. A human-AI assistant approach is presented.
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
COMPREHENSIVE ANALYSIS OF NATURAL LANGUAGE PROCESSING TECHNIQUEJournal For Research
Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.
The paper presents a model for developing intelligent query processing in Malayalam. For this the
investigator has selected a domain as time enquiry system in Malayalam language. This work discusses
issues involved in Natural Language Processing. NLQPS is a restricted domain system, deals with the
natural Language Queries on time enquiry for different modes of transportation. The system performs a
shallow syntactic and semantic analysis of the input query. After the knowledge level understanding of the
query, the system triggers a reasoning process to determine the type of query and the result slots that are
required. The investigator tries to extract the hidden intelligent behind a Natural Language Query
submitted by a user.
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
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Ontology may be a conceptualization of a website into a human understandable, however machine-
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intentional aspects of a site, whereas the denotative part is provided by a mental object that contains
assertions about instances of concepts and relations. Semantic relation it might be potential to extract the
whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations
describe the linguistics relationships among the entities involve that is beneficial for a higher
understanding of human language. The relation can be identified from the result of concept hierarchy
extraction. The existing ontology learning process only produces the result of concept hierarchy extraction.
It does not produce the semantic relation between the concepts. Here, we have to do the process of
constructing the predicates and also first order logic formula. Here, also find the inference and learning
weights using Markov Logic Network. To improve the relation of every input and also improve the relation
between the contents we have to propose the concept of ARSRE.
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Using construction grammar in conversational systems
1. Using Construction Grammar in Conversational Systems Marie-Claire Jenkins, PhD Thesis (High level overview)
2. Overview This thesis was motivated by the machine's limitations in understanding natural language and in forming responses. The limitations and complexities of current search engine querying was also a factor. Conversational systems are good for testing possible solutions and are useful on the web. We used methods that are not common in these systems: - Construction Grammar (CxG) - OWL ontologies - Lexical semantics - A new stemmer (Uea-Lite)
3.
4. Things I covered in my research: - Natural language understanding - Natural language generation - Human computer interaction - Service oriented systems Things I didn't cover in my research: - Knowledge acquisition - Open domains - Affective behaviour - Everything else
5. Conversational systems They are more commonly referred to as "chatbots" or “ Artificial Conversational Entities ” They converse with a user in natural language and simulate a human-human conversation. They need to: - "Understand ” the user input - Retrieve relevant information - Generate a natural language response There are 3 different kinds of chatbots...
6. Social chatbots Their purpose is to chat freely about anything at all with a user, much like you would with a friend. They are used online for fun.
7. Educational chatbots Their purpose is to help the user learn about something such as a new language, history or geography. They are often used in schools
8. Service oriented chatbots Their purpose is to help customers find their way around the website and also to answer questions about their products & services.
9. How they work There are a variety of methods used but the most popular are: - Database driven - AIML (artificial intelligence markup language, xml based) - Canned responses - Stochastic methods - Supervised learning - Named entity recognition - Templates
10. Phrase-based systems “ Phrase Based systems” are seen as generalized templates at the sentence level (like phrase structure rules) or at the discourse level. 1- Phrasal pattern selected [subject noun verb] 2 - Each part of the pattern is expanded [noun modifiers] 3 - When each phrasal pattern has been replaced by 1+ words –END They are very difficult to build because the phrasal interrelationships must be clearly specified otherwise there can be inappropriate phrase expansions.
11. Feature-based systems In “Feature-based systems” each possible alternative is represented by a feature and each sentence is specified by them. Sentence generation is achieved by using all of these features until the sentence is determined. Features may include: positive/negative, past/present, statement/question… Strength: any distinction in language can be a feature Weakness: very hard to maintain feature inter-relationships and the control of feature selection.
12. Observations from live data Tests on dialogue from the human-human customer service system on a large commercial website reveal that there is no consistency in language or phrase formulation. There is a very small amount of Formulaic language (canned responses). A question was never formulated in the same way and never answered in the same way (apart from formulaicity). This makes it hard for us to produce templates or anticipate user utterances.
13. More Limitations Main issues with existing systems: - Scalability - Knowledge & information storage - User input disambiguation - Response generation (word order, vocabulary, etc...) - Knowledge/information retrieval - Anaphora - Managing the dialogue - Displaying appropriate behaviour (affective issues) - Knowledge assimilation - Evaluation
14. Turing test “ A machine is termed capable of thinking if it can, under certain prescribed conditions imitate a human by answering questions sufficiently well to deceive a human questioner for a reasonable period of time. ” (Turing) Objections to the test include proving intelligence, "understanding" and other things. My personal opinion has changed since the beginning of my PhD research: “ The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. ” (Dijkstra)
17. Loebner prize This yearly contest is run by Hugh Loebner who has offered a $100,000 prize for the 1st chatbot to pass the Turing test This test is controversial. Marvin Minsky said : “ I do hope that someone will volunteer to violate this proscription so that Mr. Loebner will indeed revoke his stupid prize, save himself some money, and spare us the horror of this obnoxious and unproductive annual publicity campaign. ”
19. John We built a conversational chatbot and entered it into the Loebner prize (2006). It was designed & built in 2 months and operated on a closed domain. Reason: to run on a small database requiring little manual labour. We used ngrams, weighted responses, a vector approach, perl, Brill, UEA-Lite, wildcards, AIML We were a finalist and we learned that: - A small database worked for a small amount of time - A database system makes for laborious build and limited information (well used systems work much better) - Template methods are limited - Canned responses are awkward - AIML is restrictive
20. KIA: the HCI tests We designed a system made to research human-machine interaction and human behaviour: this is a test on humans and not the system We included functions that were meant to test user persistence with query repair, emotive response, language etc... Results: users persist, are emotive, sensitive to interface design and more. Details available in our paper
22. Databases vs OWL ontologies: Databases focus on local semantics and ontologies on global semantics. In ontologies the semantics are explicit and in databases implicit. Ontologies allow data to be reused whereas database schemas cannot be reused. Ontologies are portable between websites to facilitate maintenance and construction Restrictions in databases do not allow for all of the necessary relations to be built into the data.
24. OWL flavour We used OWL (Web Ontology Language) as it is more expressive than other semantic web languages and is built to enable ontologies to be created easily. It is a semantic markup language and an extension of RDF (Resource Description Framework). There are different subsets of OWL: OWL Full, OWL Lite and OWL DL (Description Logic). We chose to use OWL DL.
25. Why Ontologies & why OWL DL? Taxonomies are also not as expansive as ontologies. “ At one extreme there are ontologies and the other mind maps and pathfinder networks, and in between taxonomies and browserable hierarchies ”. (Brewtser and Wilkes) Ontologies have a greater potential for inference and a greater degree of formality. OWL DL has stricter restrictions which are necessary in our type of system. It has maximum expressiveness without losing computational completeness (all entailments are will be computed) and decidability (all computations will finish in finite time) of reasoning systems.
27. What do we store in there? - All of the domain knowledge (e.g all about Koalas) - The collection of constructions (commonly used when discussing koalas) - Canned responses (formulaic language)
29. Construction Grammar It is a cognitive linguistic method and it is: - Constraint based - Generative - Non-derivational - A monostratal grammatical model - Incorporates the cognitive and interactional foundations of language - Consists of taxonomies of families of constructions - Uses entire constructions as the primary unit of grammar - Is a pairing of form and meaning (metonomic) - Frames used in CxG != regular frames because the argument structure types invoke frames which designate event types - The verb alone is not the main unit of meaning, the construction itself is
31. Example of CxG Semantics: relational predicate involving a singer Syntactics: predicate requires arguments and ``Heather'' is the subject Generative Grammar Construction Grammar
32. Advantages of CxG - Adapts to changing language patterns easily - Takes into consideration both semantics and syntactics - Constructions are easier to manage than words as the atomic unit - Allows for integration into bigger collections of constructions - Can be computed
33. UEA-Lite stemmer After testing the system with all available stemmers, we realised that we needed to design our own to facilitate topic/construction detection. UEA-Lite stems conservatively to orthographically correct word forms and recognizes words which do not need to be stemmed. There is a Perl, Java and Ruby version More information here (an updated paper to follow soon)
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35. Algorithms - Jaccard Distance to weight the constructions (how often different constructions are found in conjunction, partial or complete) - Naive Bayes algorithm clusters all of the constructions according to their different features in our training set (requires little training data) Once the data has been processed through the Naive Bayes algorithm we know which constructions are often found with others, and in what order. We not only look at the syntax but also at the semantic aspect both in isolation and in conjunction with each other. The role of the classifier is to determine which categories future constructions belong to, and also to tell us which constructions are a likely match to a query.
36. Naïve Bayes for CxG P (Constructions) doesn't change over time. Naive Bayes estimates a multinomial distribution over categories, which is the prior distribution of categories We can therefore say that: Best category [ArgaMax cat in cats] = P (constructions ¦ cat) (P (cat)) If c1, c2, ... cn are the constructions in the document, then: Best category [ArgaMax cat in cats] = P(c1|cat)*P(c2|cat)*...*P(cn|cat)*P(cat)
37. System diagram There are many more components to the system than presented in this presentation as you can see.
38. Evaluation methods There are not any robust evaluation methods for conversational systems but we found that a mixture of the following worked well: - Human evaluation (feedback form) - " Pourpre ” to evaluate sentence complexity (Jimmy Lin) - Expected vs Given response score Evaluation is not finished as yet but the initial results are encouraging with good knowledge retrieval and construction selection.
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40. Fluid construction Grammar (FCG) (also didn't work!) - Bi-directional (using rules) - Selects meanings and maps them into the real world. - "fluid" because it takes into consideration the fact that users change and update their grammars often. - User input can be broken down syntactically in order to gain meaning from the grammatical components, whilst also being able to map the semantic relationships BUT : not developed enough to work well in our system Also: bi-directional rules are very hard to write
41. Some Outcomes & Learnings - Construction Grammar is a useful method for NLU & NLG - OWL ontologies are well suited to these systems - Stemming affects the system greatly - Fluid CxG is not practical at this time - Better evaluation methods need to be developed - Turing test is not useful as it does not prove machine intelligence or understanding - User perception is a primordial area of research
42. Applications & Future work - Assisted search - Summarization systems - Content creation - Speech systems - Sentiment analysis - More powerful AI module - Anaphora resolution - Open domain testing - Improved machine learning - Further work on query disambiguation methods
43. Thank you Find me at: http://www.scienceforseo.com http://twitter.com/missmcj Google reader