From the "Natural Language Processing" LinkedIn group:
John Kontos, Professor of Artificial Intelligence
I wonder whether translating into formal logic is nothing more than transliteration which simply isolates the part of the text that can be reasoned upon using the simple inference mechanism of formal logic. The real problem I think lies with the part of text that CANNOT be translated one the one hand and the one that changes its meaning due to civilization advances. My own proposal is to leave NL text alone and try building inference mechanisms for the UNTRANSLATED text depending on the task requirements.
All the best
John"
A Natural Logic for Artificial Intelligence, and its Risks and Benefits gerogepatton
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven” natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently” processed by computers, using the semantic representations of the phrases of the fragment.
Classical logic has a serious limitation in that it cannot cope with the issues of vagueness and uncertainty
into which fall most modes of human reasoning. In order to provide a foundation for human knowledge
representation and reasoning in the presence of vagueness, imprecision, and uncertainty, fuzzy logic
should have the ability to deal with linguistic hedges, which play a very important role in the modification
of fuzzy predicates. In this paper, we extend fuzzy logic in narrow sense with graded syntax, introduced by
Nova´k et al., with many hedge connectives. In one case, each hedge does not have any dual one. In the
other case, each hedge can have its own dual one. The resulting logics are shown to also have the Pavelkastyle
completeness.
Analogy is one of the most studied representatives of a family of non-classical forms of reasoning working across different domains, usually taken to play a crucial role in creative thought and problem-solving. In the first part of the talk, I will shortly introduce general principles of computational analogy models (relying on a generalization-based approach to analogy-making). We will then have a closer look at Heuristic-Driven Theory Projection (HDTP) as an example for a theoretical framework and implemented system: HDTP computes analogical relations and inferences for domains which are represented using many-sorted first-order logic languages, applying a restricted form of higher-order anti-unification for finding shared structural elements common to both domains. The presentation of the framework will be followed by a few reflections on the "cognitive plausibility" of the approach motivated by theoretical complexity and tractability considerations.
In the second part of the talk I will discuss an application of HDTP to modeling essential parts of concept blending processes as current "hot topic" in Cognitive Science. Here, I will sketch an analogy-inspired formal account of concept blending —developed in the European FP7-funded Concept Invention Theory (COINVENT) project— combining HDTP with mechanisms from Case-Based Reasoning.
A Natural Logic for Artificial Intelligence, and its Risks and Benefits gerogepatton
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven” natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently” processed by computers, using the semantic representations of the phrases of the fragment.
Classical logic has a serious limitation in that it cannot cope with the issues of vagueness and uncertainty
into which fall most modes of human reasoning. In order to provide a foundation for human knowledge
representation and reasoning in the presence of vagueness, imprecision, and uncertainty, fuzzy logic
should have the ability to deal with linguistic hedges, which play a very important role in the modification
of fuzzy predicates. In this paper, we extend fuzzy logic in narrow sense with graded syntax, introduced by
Nova´k et al., with many hedge connectives. In one case, each hedge does not have any dual one. In the
other case, each hedge can have its own dual one. The resulting logics are shown to also have the Pavelkastyle
completeness.
Analogy is one of the most studied representatives of a family of non-classical forms of reasoning working across different domains, usually taken to play a crucial role in creative thought and problem-solving. In the first part of the talk, I will shortly introduce general principles of computational analogy models (relying on a generalization-based approach to analogy-making). We will then have a closer look at Heuristic-Driven Theory Projection (HDTP) as an example for a theoretical framework and implemented system: HDTP computes analogical relations and inferences for domains which are represented using many-sorted first-order logic languages, applying a restricted form of higher-order anti-unification for finding shared structural elements common to both domains. The presentation of the framework will be followed by a few reflections on the "cognitive plausibility" of the approach motivated by theoretical complexity and tractability considerations.
In the second part of the talk I will discuss an application of HDTP to modeling essential parts of concept blending processes as current "hot topic" in Cognitive Science. Here, I will sketch an analogy-inspired formal account of concept blending —developed in the European FP7-funded Concept Invention Theory (COINVENT) project— combining HDTP with mechanisms from Case-Based Reasoning.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML
The traditional semantics for First Order Logic (sometimes called Tarskian semantics) is based on the notion of interpretations of constants. Herbrand semantics is an alternative semantics based directly on truth assignments for ground sentences rather than interpretations of constants. Herbrand semantics is simpler and more intuitive than Tarskian semantics; and, consequently, it is easier to teach and learn. Moreover, it is more expressive. For example, while it is not possible to finitely axiomatize integer arithmetic with Tarskian semantics, this can be done easily with Herbrand Semantics. The downside is a loss of some common logical properties, such as compactness and completeness. However, there is no loss of inferential power. Anything that can be proved according to Tarskian semantics can also be proved according to Herbrand semantics. In this presentation, we define Herbrand semantics; we look at the implications for research on logic and rules systems and automated reasoning; and and we assess the potential for popularizing logic.
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Waqas Tariq
A \"sentence pattern\" in modern Natural Language Processing is often considered as a subsequent string of words (n-grams). However, in many branches of linguistics, like Pragmatics or Corpus Linguistics, it has been noticed that simple n-gram patterns are not sufficient to reveal the whole sophistication of grammar patterns. We present a language independent architecture for extracting from sentences more sophisticated patterns than n-grams. In this architecture a \"sentence pattern\" is considered as n-element ordered combination of sentence elements. Experiments showed that the method extracts significantly more frequent patterns than the usual n-gram approach.
MORPHOLOGICAL SEGMENTATION WITH LSTM NEURAL NETWORKS FOR TIGRINYAijnlc
Morphological segmentation is a fundamental task in language processing. Some languages, such as
Arabic and Tigrinya,have words packed with very rich morphological information.Therefore, unpacking
this information becomes a necessary taskfor many downstream natural language processing tasks. This
paper presents the first morphological segmentation research forTigrinya. We constructed a new
morphologically segmented corpus with 45,127 manually segmented tokens. Conditional random fields
(CRF) and window-based longshort-term memory (LSTM) neural networkswere employed separately to
develop our boundary detection models. We appliedlanguage-independent character and substring features
for the CRFand character embeddings for the LSTM networks. Experimentswere performed with four
variants of the Begin-Inside-Outside (BIO) chunk annotation scheme. We achieved 94.67% F1 scoreusing
bidirectional LSTMs with fixed-sizewindow approach to morphemeboundary detection.
Research in Artificial Intelligence has built upon the tools and techniques of many different disciplines, including formal logic, probability theory, decision theory, management science, linguistics and philosophy. However, the application of these disciplines in AI has necessitated the development of many enhancements and extensions.
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Ambiguous Requirements – Translating the message from C-level to implementationGeorgina Tilby
Using a mathematical model used for cost projection, ambiguities result in increased margins of error.
Using the same reasoning, it can be shown that ambiguities directly result in divergence from the high-level vision.
By the time the implementation is reviewed by the C-Level executive, it is too late, and correcting the ‘defects’ will take time and money.
Delivering quality projects, on time and to specification, requires eliminating this divergence – hence, better and less ambiguous requirements are the order of the day.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML
The traditional semantics for First Order Logic (sometimes called Tarskian semantics) is based on the notion of interpretations of constants. Herbrand semantics is an alternative semantics based directly on truth assignments for ground sentences rather than interpretations of constants. Herbrand semantics is simpler and more intuitive than Tarskian semantics; and, consequently, it is easier to teach and learn. Moreover, it is more expressive. For example, while it is not possible to finitely axiomatize integer arithmetic with Tarskian semantics, this can be done easily with Herbrand Semantics. The downside is a loss of some common logical properties, such as compactness and completeness. However, there is no loss of inferential power. Anything that can be proved according to Tarskian semantics can also be proved according to Herbrand semantics. In this presentation, we define Herbrand semantics; we look at the implications for research on logic and rules systems and automated reasoning; and and we assess the potential for popularizing logic.
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Waqas Tariq
A \"sentence pattern\" in modern Natural Language Processing is often considered as a subsequent string of words (n-grams). However, in many branches of linguistics, like Pragmatics or Corpus Linguistics, it has been noticed that simple n-gram patterns are not sufficient to reveal the whole sophistication of grammar patterns. We present a language independent architecture for extracting from sentences more sophisticated patterns than n-grams. In this architecture a \"sentence pattern\" is considered as n-element ordered combination of sentence elements. Experiments showed that the method extracts significantly more frequent patterns than the usual n-gram approach.
MORPHOLOGICAL SEGMENTATION WITH LSTM NEURAL NETWORKS FOR TIGRINYAijnlc
Morphological segmentation is a fundamental task in language processing. Some languages, such as
Arabic and Tigrinya,have words packed with very rich morphological information.Therefore, unpacking
this information becomes a necessary taskfor many downstream natural language processing tasks. This
paper presents the first morphological segmentation research forTigrinya. We constructed a new
morphologically segmented corpus with 45,127 manually segmented tokens. Conditional random fields
(CRF) and window-based longshort-term memory (LSTM) neural networkswere employed separately to
develop our boundary detection models. We appliedlanguage-independent character and substring features
for the CRFand character embeddings for the LSTM networks. Experimentswere performed with four
variants of the Begin-Inside-Outside (BIO) chunk annotation scheme. We achieved 94.67% F1 scoreusing
bidirectional LSTMs with fixed-sizewindow approach to morphemeboundary detection.
Research in Artificial Intelligence has built upon the tools and techniques of many different disciplines, including formal logic, probability theory, decision theory, management science, linguistics and philosophy. However, the application of these disciplines in AI has necessitated the development of many enhancements and extensions.
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Ambiguous Requirements – Translating the message from C-level to implementationGeorgina Tilby
Using a mathematical model used for cost projection, ambiguities result in increased margins of error.
Using the same reasoning, it can be shown that ambiguities directly result in divergence from the high-level vision.
By the time the implementation is reviewed by the C-Level executive, it is too late, and correcting the ‘defects’ will take time and money.
Delivering quality projects, on time and to specification, requires eliminating this divergence – hence, better and less ambiguous requirements are the order of the day.
A NATURAL LOGIC FOR ARTIFICIAL INTELLIGENCE, AND ITS RISKS AND BENEFITSijwscjournal
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner
enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural
logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven”
natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but
indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently”
processed by computers, using the semantic representations of the phrases of the fragment.
A NATURAL LOGIC FOR ARTIFICIAL INTELLIGENCE, AND ITS RISKS AND BENEFITSijasuc
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner
enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural
logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven”
natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but
indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently”
processed by computers, using the semantic representations of the phrases of the fragment.
AN ONTOLOGICAL ANALYSIS AND NATURAL LANGUAGE PROCESSING OF FIGURES OF SPEECHgerogepatton
The purpose of the current paper is to present an ontological analysis to the identification of a particular type of prepositional figures of speech via the identification of inconsistencies in ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to describe real world events and activities. However, one aspect that makes a prepositional noun phrase poetical is that the latter suggests a semantic relationship between concepts that does not exist in the real world. The current paper shows that a set of rules based on WordNet classes and an ontology representing human behaviour and properties, can be used to identify figures of speech due to the discrepancies in the semantic relations of the concepts involved. Based on this realization, the paper describes a method for determining poetic vs. non-poetic prepositional figures of speech, using WordNet class hierarchies. The paper also addresses the problem of inconsistency resulting from the assertion of figures of speech in ontological knowledge bases, identifying the problems involved in their representation. Finally, it discusses how a contextualized approach might help to resolve this problem.
AN ONTOLOGICAL ANALYSIS AND NATURAL LANGUAGE PROCESSING OF FIGURES OF SPEECHijaia
The purpose of the current paper is to present an ontological analysis to the identification of a particular type of prepositional figures of speech via the identification of inconsistencies in ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to describe real world events and activities. However, one aspect that makes a prepositional noun phrase poetical is that the latter suggests a semantic relationship between concepts that does not exist in the real world. The current paper shows that a set of rules based on WordNet classes and an ontology representing human behaviour and properties, can be used to identify figures of speech due to the discrepancies in the semantic relations of the concepts involved. Based on this realization, the paper describes a method for determining poetic vs. non-poetic prepositional figures of speech, using WordNet class hierarchies. The paper also addresses the problem of inconsistency resulting from the assertion of figures of speech in ontological knowledge bases, identifying the problems involved in their representation. Finally, it discusses how a contextualized approach might help to resolve this problem
Natural Language Ambiguity and its Effect on Machine LearningIJMER
"Natural language processing" here refers to the use and ability of systems to process
sentences in a natural language such as English, rather than in a specialized artificial computer
language such as C++. The systems of real interest here are digital computers of the type we think of as
personal computers and mainframes. Of course humans can process natural languages, but for us the
question is whether digital computers can or ever will process natural languages. We have tried to
explore in depth and break down the types of ambiguities persistent throughout the natural languages
and provide an answer to the question “How it affects the machine translation process and thereby
machine learning as whole?” .
Theories of induction in psychology and artificial intelligence assume that the process leads from observation and knowledge to the formulation of linguistic conjectures. This paper proposes instead that the process yields mental models of phenomena. It uses this hypothesis to distinguish between deduction, induction, and creative forms of thought. It shows how models could underlie inductions about specific matters. In the domain of linguistic conjectures, there are many possible inductive generalizations of a conjecture. In the domain of models, however, generalization calls for only a single operation: the addition of information to a model. If the information to be added is inconsistent with the model, then it eliminates the model as false: this operation suffices for all generalizations in a Boolean domain. Otherwise, the information that is added may have effects equivalent (a) to the replacement of an existential quantifier by a universal quantifier, or (b) to the promotion of an existential quantifier from inside to outside the scope of a universal quantifier. The latter operation is novel, and does not seem to have been used in any linguistic theory of induction. Finally, the paper describes a set of constraints on human induction, and outlines the evidence in favor of a model theory of induction.
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Lecture 2: From Semantics To Semantic-Oriented Applications
1. Semantic Analysis in Language Technology
Lecture 2: From Semantics to Semantic-Oriented Applications
Course Website: http://stp.lingfil.uu.se/~santinim/sais/sais_fall2013.htm
MARINA SANTINI
PROGRAM: COMPUTATIONAL LINGUISTICS AND LANGUAGE TECHNOLOGY
DEPT OF LINGUISTICS AND PHILOLOGY
UPPSALA UNIVERSITY, SWEDEN
14 NOV 2013
2. From Formal Systems to Natural Language Semantics
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The past:
Aristotelean Logic
Prepositional Logic
[huge temporal gap]
Predicate Logic (FOL & co.)
Formal Semantics
The present:
Computational Semantics & Semantic-Oriented Applications
The future:
Actionable Intelligence
Lecture 2: From Semantics to Applications
3. Aristotelian Logic
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The fundamental assumption behind the theory is that propositions are
composed of two terms – hence the name "two-term theory" or "term logic"
– and that the reasoning process is in turn built from propositions:
Aristotle distinguishes singular terms such as Socrates and general terms such as Greeks.
Aristotle further distinguishes (a) terms that could be the subject of predication, and (b)
terms that could be predicated of others by the use of the copula ("is a").
A proposition consists of two terms, in which one term (the "predicate") is
"affirmed" or "denied" of the other (the "subject"), and which is capable of
truth or falsity.
Socrates is a man
Socrates is not immortal
The syllogism is an inference in which one proposition (the "conclusion")
follows of necessity from two others (the "premises").
Socrates is a man,
all men are mortal,
therefore Socrates is mortal = new knowledge (inferential knowledge)
Lecture 2: From Semantics to Applications
4. Syllogistic fallacies
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People often make mistakes when reasoning
syllogistically and mathematically with natural language:
• A=B
• B=C
• A=C
some cats (A) are black things (B),
some black things (B) are televisions (C),
it does not follow from the parameters that some cats (A) are
televisions (C).
Existential fallacy (use of quantifiers)
The existential fallacy, or existential instantiation, is a formal
fallacy: "Everyone in the room is pretty and smart". It does not
imply that there is a pretty, smart person in the room, because it
does not state that there is a person in the room.
Lecture 2: From Semantics to Applications
5. Prepositional Logic
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It was developed into a formal logic by Chrysippus and
expanded by the Stoics.
The logic was focused on propositions.
This advancement was different from the traditional syllogistic
logic which was focused on terms.
It represents any given proposition with a letter.
It requires that all propositions have exactly one of two truth-
values: true or false.
To take an example, let be the proposition that it is raining outside.
This will be true if it is raining outside and false otherwise.
Lecture 2: From Semantics to Applications
6. The father of Predicate Logic
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In 1879 Frege published his Begriffsschrift (Concept
Script). This introduced a calculus, a method of
representing statements by the use of quantifiers
and variables.
Lecture 2: From Semantics to Applications
7. Predicate Logic (aka FOL, etc.)
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Predicate logic is also known as first-order predicate
calculus, the lower predicate calculus,quantification
theory, and first-order logic.
First-order logic is a formal system used
in mathematics, philosophy, linguistics, and computer
science.
First-order logic is distinguished from propositional
logic by its use of quantified variables.
First-order logic is distinguished from propositional
logic by its use of quantified variables.
Lecture 2: From Semantics to Applications
8. Quantifiers
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The two fundamental kinds of quantification
in predicate logic are universal
quantification and existential quantification. The
traditional symbol for the universal quantifier "all" is
"∀", an inverted letter "A", and for the existential
quantifier "exists" is "∃", a rotated letter "E".
Lecture 2: From Semantics to Applications
9. Propositional Logic vs Predicate Logic
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A predicate takes an entity or entities in the domain of discourse as input and
outputs either True or False.
Consider the two sentences "Socrates is a philosopher" and "Plato is a philosopher".
In propositional logic, these sentences are viewed as being unrelated and are denoted, for example,
by p and q. However, the predicate "is a philosopher" occurs in both sentences which have a common
structure of "a is a philosopher". The variable a is instantiated as "Socrates" in first sentence and is
instantiated as "Plato" in the second sentence
"There exist a such that a is a philosopher" .
Predicates can be also compared.
Ex "if a is a philosopher, then a is a scholar". This formula is a conditional statement with "a is
philosopher" as hypothesis and "a is a scholar" as conclusion.
The truth of this formula depends on which object is denoted by a, and on the interpretations of the
predicates "is a philosopher" and "is a scholar".
Variables can be quantified over. "For every a, if a is a philosopher, then a is a scholar". The universal
quantifier "for every" in this sentence expresses the idea that the claim "if a is a philosopher, then a is
a scholar" holds for all choices of a.
Lecture 2: From Semantics to Applications
a
10. Formal Semantics (wikipedia)
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In linguistics, formal semantics seeks to understand linguistic
meaning by constructing precise mathematical models of the
principles that speakers use to define relations between
expressions in a natural language and the world which
supports meaningful discourse.
The mathematical tools used are the confluence of formal
logic and formal language theory, especially lambda calculus.
Linguists rarely employed formal semantics until Richard
Montague showed how English (or any natural
language) could be treated like a formal language. His
contribution to linguistic semantics, which is now known as
Montague grammar, was the basis for further developments.
Lecture 2: From Semantics to Applications
11. Translating Natural Language to Formal Language by:
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Lamba calculus:
is a formal system in mathematical
logic and computer science for
expressing computation based on
function abstraction and application
via variable binding and substitution.
(Cf also J&M: 593)
Prolog:
Prolog is a general purpose logic
programming language associated
with artificial
ntelligence and computational
linguistics.
Prolog has its roots in first-order logic,
a formal logic, and unlike many
other programming languages, Prolog
is declarative: the program logic is
expressed in terms of relations,
represented as facts and rules.
Lecture 2: From Semantics to Applications
Top-down rule-based systems
13. Stumbling block: meaning is not always compositional…
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Lecture 2: From Semantics to Applications
14. Multi-Word Expressions
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MWEs (a.k.a multiword units or MUs) are lexical
units encompassing a wide range of linguistic
phenomena, such as:
idioms (e.g. kick the bucket = to die),
collocations (e.g. cream tea = a small meal eaten in Britain,
with small cakes and tea),
regular compounds (cosmetic surgery),
graphically unstable compounds (e.g. self-contained <> self
contained <> selfcontained - all graphical variants have huge
number of hits in Google),
light verbs (e.g. do a revision vs. revise),
lexical bundles (e.g. in my opinion), etc.
Lecture 2: From Semantics to Applications
15. Stumbling Block: Ambiguity
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Lexical ambiguity: ex polisemy
Ex: bank
Referential ambiguity: ex anaphoric ambiguity
… it was funded by a tycoon
Scopal ambiguity:
I can’t find a piece of paper
(a particular piece of paper or any piece of paper? Existential or
universal quantifier "∀”or "∃“?)
Lecture 2: From Semantics to Applications
16. Computational Semantics (wikipedia)
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Computational semantics is the study of how to automate the process of
constructing and reasoning with meaning representations of natural
language expressions. It consequently plays an important role in natural
language processing and computational linguistics.
Some traditional topics of interest are:
construction of meaning representations,
semantic underspecification,
anaphora resolution,
presupposition projection,
quantifier scope resolution.
Methods employed usually draw from formal semantics or statistical
semantics. Computational semantics has points of contact with the areas of
lexical semantics (word sense disambiguation and semantic role labeling),
discourse semantics, knowledge representation and automated reasoning
…
Lecture 2: From Semantics to Applications
17. What is Semantics? ---- What is LT?
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Students’ intuition about
semantics:
1.
2.
3.
4.
5.
6.
7.
Meaning of language
(words, phrases, etc.)
Break down complex
meaning into simpler
blocks of meaning
Content understanding
Disambiguation
Understanding a phrase
Understanding the
meaning of phrases
depending on different
contexts
Meaning and
connotation
Lecture 2: From Semantics to Applications
Language technology is often
called human language
technology (HLT) or natural
language processing (NLP) and
consists of computational
linguistics (or CL) and speech
technology as its core but
includes also many application
oriented aspects of them.
Language technology is closely
connected to computer
science and general
linguistics. (wikipedia)
Must add:
•Statistics
•Machine learning
18. What shall we keep from the past?
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Computational semantics must be….
Lecture 2: From Semantics to Applications
19. Computational semantics must address open issues:
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Ambiguity
Overcome compositionality
Etc.
Lecture 2: From Semantics to Applications
20. Our definition of semantics for LT must include:
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1.
2.
3.
4.
5.
6.
7.
Meaning of language (words,
phrases, etc.)
Break down complex meaning
into simpler blocks of meaning
Content understanding
Disambiguation
Understanding a phrase
Understanding the meaning of
phrases depending on different
contexts
Meaning and connotation
Semantics for Language Technolgy
must now take also these aspects into
account.
Lecture 2: From Semantics to Applications
Continuity with the past approaches
Must be computationally tractable
More advanced than past systems:
Must address ambiguity
Must address non
compositional meaning
Above all, must tackle new media.
In less than 50 years, new media (internet,
web, social networks) have completely
scrambled ”traditional” semantics and
human communication by creating :
•New meanings (sentiment, opionion, etc)
•New language (unconvetional texts and
syntax and many sublanguages, like
tweets, FB posts, etc.)
•Big amounts of wild data
21. In conclusion
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More than creating a ”understanding system”, currently the stress in
how to automatically extract meaningful and actionable information
depending on specifc tasks….
Lecture 2: From Semantics to Applications
22. Visual Insight into big data around us…
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Big Data Video: http://youtu.be/qqfeUUjAIyQ (2:21 min)
Lecture 2: From Semantics to Applications
23. New meanings: the so-called sentiment
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Sentiment Analysis’s purpose: detect and extract
emotions, attitudes, opnions from text… People
behaviour and choices (politics, products, reactions)
are driven by sentiment rather than ”sensibility”
(Sense and Sensibility by J. Austin well describe
these two opposite behaviours)
A basic ML algorithm underlying many (but not all)
applications detecting sentiments: Daniel Jurafsky,
Coursera, NLP – Stanford University (video, 13 min)
Lecture 2: From Semantics to Applications
24. Conclusions
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Think about semantics, computational semantics
and big data
Think how ML is important for semantic-oriented
applications (be proud of the many things you
learned during the previous course)
Next time we will continue with Sentiment Analysis,
which is a semantic-oriented application…
Lecture 2: From Semantics to Applications
25. 25
This is the end… Thanks for your attention !
Lecture 2: From Semantics to Applications