Working out when events in a text happen is difficult. Many have tried over the past decade but the state of the art has not advanced.
After introducing a few fundamental concepts for dealing with time in language, we work out what makes this task so difficult, and then identify two common causes of temporal ordering difficulty and describe how to overcome them.
Full document: http://derczynski.com/sheffield/papers/derczynski-phdthesis.pdf
Bridging the gap between AI and UI - DSI Vienna - full versionLiad Magen
This is a summary of the latest research on model interpretability, including Recurrent neural networks (RNN) for Natural Language Processing (NLP) in terms of what's in an RNN.
In addition, it contains suggestion to improve machine learning based user interface, to engage users and encourage them to contribute data to adapt the models to them.
This chapter discusses definitions of discourse and discourse analysis, including "little d" discourse referring to language in context and "big D" discourse as specialized language of social groups. It outlines structural and functional approaches to discourse analysis and describes various disciplines and main approaches. Context and models of communication are examined, including Hymes' 16 contextual features and Halliday's three parameters of context. The development of the concept of communicative competence from Hymes to Canale and Swain to Celce-Murcia is summarized.
This document discusses the differences between written and spoken discourse. It notes that written discourse can be referred back to, while spoken discourse must be understood immediately. Spoken discourse involves variations in speed, loudness, gestures, intonation, pauses and rhythm. Grammatically, spoken discourse contains fewer subordinate clauses and more active verbs. Lexically, spoken discourse uses more pronouns, repetitions, first person references and active verbs. Structurally, spoken discourse is more fragmented with simple sentences and coordination. Functionally, written discourse allows storage of information over time and space while spoken discourse is used primarily for interaction and relationships.
The document discusses various topics related to second language acquisition including learner strategies, language learning styles, linguistic concepts like lemmas and lexicons, and theories of language acquisition such as Krashen's Monitor Model and the Natural Order Hypothesis. It provides definitions and explanations of these key concepts and frameworks in second language acquisition research.
The document discusses discourse analysis and how language users interpret meaning beyond just recognizing grammatical structures. It examines how coherence and cohesion allow readers to understand fragmented or ungrammatical texts by filling in gaps. Conversational interactions are analyzed in terms of turn-taking, completion points, and the cooperative principle of relevance, brevity, and honesty. Discourse analysis investigates how language is used in context.
The document discusses discourse markers, which are words like "however" and "although" that are used to link ideas between sentences and paragraphs. It provides examples of common discourse markers used to indicate relationships like addition, contrast, concession, and conditions. It also explains that discourse markers can be used at the start of sentences or clauses separated by semicolons. Paragraphs are similarly linked using discourse markers to show reinforcement, contrast, or concession between ideas.
Discourse and Genre (the relationship between discourse and genre) Aticka Dewi
We provide some questions to make the discussion clearer
1. What is discourse?
Discourse is the use of language in text and context
2. What is genre?
Genre in linguistics refers to the type and structure of language typically used for a particular purpose in a particular context.
3. What is relationship between discourse and genre?
Discourse analysis is genre analysis. When we analyze discourses, of course we will specify them into more specific types from the characteristics of each discourse. For exampleThe specific type of discourses is called as genre.
4. Why should we use genre to analyze discourse?
Discourse is language in use. It is huge and almost unlimited. So, when we want to analyze discourses, we need a limitation to limit the unlimited things. Here, we use an analogy for this statement. (slide 11,12)
Genre provides limit in discourse.
That is why genre is used to help us divining and analyzing the discourses.
5. How do we analyze discourse through genre?
Example: text “Forklift fatty Improving”.
----------
The text is taken from the newspaper report. As we see in the language features and structures, we can divine it into recount text. It is non fiction, because it is based on real event. And it is written. So, we can say that this discourse has written non-fiction recount genre.
But, we cannot make sure that a type of discourse always has the same characteristics, because discourse is neither absolutely homogenous nor absolutely heterogeneous. Discourse is sometimes heterogeneous. Here, we provide two videos which have the same genre, but quite different in terms of language features and structures.
---------VIDEO
From the videos, we can feel that the first and the second videos are quite different. The structure in the first video is introduction (addressing, personal value), content (some important issues, e.g: financial issues, goals of America, ), closing (hope for American future, blessing). The language features used in the first video is more formal, present tense. The atmosphere created is formal.
From the second video, the structure is introduction (personal value without addressing), content (some goals), closing (. The language features used in the video is mixing, unclear and needs more understanding. The atmosphere created is a bit humorous.
Although they have different characteristics, they have the same genre in term of purpose, that is political genre.
From those videos, we can conclude that we cannot stick to an idea that a genre of discourse always has the same characteristics. AGAIN, discourse is neither absolutely homogenous nor absolutely heterogeneous.
Bridging the gap between AI and UI - DSI Vienna - full versionLiad Magen
This is a summary of the latest research on model interpretability, including Recurrent neural networks (RNN) for Natural Language Processing (NLP) in terms of what's in an RNN.
In addition, it contains suggestion to improve machine learning based user interface, to engage users and encourage them to contribute data to adapt the models to them.
This chapter discusses definitions of discourse and discourse analysis, including "little d" discourse referring to language in context and "big D" discourse as specialized language of social groups. It outlines structural and functional approaches to discourse analysis and describes various disciplines and main approaches. Context and models of communication are examined, including Hymes' 16 contextual features and Halliday's three parameters of context. The development of the concept of communicative competence from Hymes to Canale and Swain to Celce-Murcia is summarized.
This document discusses the differences between written and spoken discourse. It notes that written discourse can be referred back to, while spoken discourse must be understood immediately. Spoken discourse involves variations in speed, loudness, gestures, intonation, pauses and rhythm. Grammatically, spoken discourse contains fewer subordinate clauses and more active verbs. Lexically, spoken discourse uses more pronouns, repetitions, first person references and active verbs. Structurally, spoken discourse is more fragmented with simple sentences and coordination. Functionally, written discourse allows storage of information over time and space while spoken discourse is used primarily for interaction and relationships.
The document discusses various topics related to second language acquisition including learner strategies, language learning styles, linguistic concepts like lemmas and lexicons, and theories of language acquisition such as Krashen's Monitor Model and the Natural Order Hypothesis. It provides definitions and explanations of these key concepts and frameworks in second language acquisition research.
The document discusses discourse analysis and how language users interpret meaning beyond just recognizing grammatical structures. It examines how coherence and cohesion allow readers to understand fragmented or ungrammatical texts by filling in gaps. Conversational interactions are analyzed in terms of turn-taking, completion points, and the cooperative principle of relevance, brevity, and honesty. Discourse analysis investigates how language is used in context.
The document discusses discourse markers, which are words like "however" and "although" that are used to link ideas between sentences and paragraphs. It provides examples of common discourse markers used to indicate relationships like addition, contrast, concession, and conditions. It also explains that discourse markers can be used at the start of sentences or clauses separated by semicolons. Paragraphs are similarly linked using discourse markers to show reinforcement, contrast, or concession between ideas.
Discourse and Genre (the relationship between discourse and genre) Aticka Dewi
We provide some questions to make the discussion clearer
1. What is discourse?
Discourse is the use of language in text and context
2. What is genre?
Genre in linguistics refers to the type and structure of language typically used for a particular purpose in a particular context.
3. What is relationship between discourse and genre?
Discourse analysis is genre analysis. When we analyze discourses, of course we will specify them into more specific types from the characteristics of each discourse. For exampleThe specific type of discourses is called as genre.
4. Why should we use genre to analyze discourse?
Discourse is language in use. It is huge and almost unlimited. So, when we want to analyze discourses, we need a limitation to limit the unlimited things. Here, we use an analogy for this statement. (slide 11,12)
Genre provides limit in discourse.
That is why genre is used to help us divining and analyzing the discourses.
5. How do we analyze discourse through genre?
Example: text “Forklift fatty Improving”.
----------
The text is taken from the newspaper report. As we see in the language features and structures, we can divine it into recount text. It is non fiction, because it is based on real event. And it is written. So, we can say that this discourse has written non-fiction recount genre.
But, we cannot make sure that a type of discourse always has the same characteristics, because discourse is neither absolutely homogenous nor absolutely heterogeneous. Discourse is sometimes heterogeneous. Here, we provide two videos which have the same genre, but quite different in terms of language features and structures.
---------VIDEO
From the videos, we can feel that the first and the second videos are quite different. The structure in the first video is introduction (addressing, personal value), content (some important issues, e.g: financial issues, goals of America, ), closing (hope for American future, blessing). The language features used in the first video is more formal, present tense. The atmosphere created is formal.
From the second video, the structure is introduction (personal value without addressing), content (some goals), closing (. The language features used in the video is mixing, unclear and needs more understanding. The atmosphere created is a bit humorous.
Although they have different characteristics, they have the same genre in term of purpose, that is political genre.
From those videos, we can conclude that we cannot stick to an idea that a genre of discourse always has the same characteristics. AGAIN, discourse is neither absolutely homogenous nor absolutely heterogeneous.
Determining the Types of Temporal Relations in DiscourseLeon Derczynski
This document discusses determining the types of temporal relations in discourse. It introduces key temporal information extraction concepts like events, temporal expressions, and links between events and times. The document also examines relation extraction challenges, the role of temporal signals and tense in modelling temporal relations, and potential areas of future work such as temporal dataset construction.
Automatic temporal ordering of events described in discourse has been of great interest in recent years. Event orderings are
conveyed in text via various linguistic mechanisms including the use of expressions such as “before”, “after” or “during”
that explicitly assert a temporal relation – temporal signals. We investigate the role of temporal signals in temporal relation extraction and provide a quantitative analysis of these expressions in the TimeBank annotated corpus.
This document discusses theories of latent semantics and social interaction. It outlines latent semantic analysis (LSA) and social network analysis (SNA) as methods to analyze meaning and interactions. It proposes meaningful interaction analysis (MIA) as a technique that combines LSA and SNA to study associative closeness structures and social relations in latent semantic spaces. Examples of applying MIA to analyze forum postings, virtual meeting attendance, and blog subscriptions are provided.
Foundations Defining Communication & Communication Study.docxbudbarber38650
Foundations:
Defining Communication &
Communication Study
Survey of Communication Study, Hahn and Paynton, chpt. 1
https://en.wikibooks.org/wiki/Survey_of_Communication_Study/Chapter_1_-_Foundations:_Defining_Communication_and_Communication_Study
Introductions
Discuss the course syllabus, assignments and online Textbook
Blackboard how-to
Communication Definitions
Communication Models
Linear Model
Transactional Model
Communication and You
Agenda
Introductions
Introduce yourself:
Name
Hometown
Year in School
Major
Dream Job
Go over syllabus and Blackboard
3
We will be using a combination of two FREE online textbooks, known as an OER (Open Educational Resources)
These are free online textbooks written by communication faculty who have allowed open use of the material.
“Survey of Communication Study” by Humboldt State University professors Laura K. Hahn and Scott T. Paynton. Last edited online 2016
https://en.wikibooks.org/wiki/Survey_of_Communication_Study
“Communication in the Real World” 2012 Creative Common License, publisher and author name removed per request
http://open.lib.umn.edu/communication/
Textbooks
If you are viewing the PPT slides on your computer or smart phone, view in the slideshow function so you will be able to interact with the various GIFs and video links that are included
Suggestion for viewing PPTs
What is involved in the process of communication?
Why is communication important?
Name people who use communication in creative ways?
Discussion:
What is Communication?
Instructor notes: These question prompts can be used as a pair activity, group activity, class discussion or “thinking point” depending on the class size and modality
6
Memes are an Example of Creative Communication
Without looking at the textbook, write down a one sentence definition of communication
To Do
70 years ago communication scholars Bruce Smith, Harold Lasswell and Ralph D. Casey stated
“Communication study is an academic field whose primary focus is ‘who says what, through what channels (media) of communication, to whom [and] what will be the results’”
(Emphasis and underline added)
Communication Definition
Smith, Lasswell and Casey
The National Communication Association states
“Communication study focuses on how people use messages to generate meaning within and across various contexts, cultures, channels and media.”
“The discipline promotes the effective and ethical practice of human communication.”
Communication Definition
National Communication Association
For the purposes of our class, the definition we will use is
“Communication is the process of using symbols to exchange meaning.”
Communication Definition
Our Textbook
A Model is a visual representation/depiction of how something works.
Models allow us to understand a process by dividing it into parts and looking at how they are related
Models of Communication
The earliest models of comm.
This document provides an overview of natural language processing (NLP) and the use of deep learning for NLP tasks. It discusses how deep learning models can learn representations and patterns from large amounts of unlabeled text data. Deep learning approaches are now achieving superior results to traditional NLP methods on many tasks, such as named entity recognition, machine translation, and question answering. However, deep learning models do not explicitly model linguistic knowledge. The document outlines common NLP tasks and how deep learning algorithms like LSTMs, CNNs, and encoder-decoder models are applied to problems involving text classification, sequence labeling, and language generation.
Semantic Relation Classification: Task Formalisation and RefinementAndre Freitas
The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded,
allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.
The document discusses dynamic systems theory and its application to second language acquisition. It describes key aspects of dynamic systems like dynamism, adaptation, heterogeneity, openness and nonlinearity. These concepts are then related to how a language classroom can be viewed as a dynamic system with interacting elements. The document also discusses factors that influence second language learning like frequency, contingency, salience, multiple cues, and interference from the first language.
The role of linguistic information for shallow language processingConstantin Orasan
The document discusses shallow language processing and summarization. It argues that while deep language understanding is limited, shallow methods can be improved by adding linguistic information. As an example, it shows how term frequency, anaphora resolution, discourse cues and genetic algorithms can select extractive summaries that better match human abstracts, without requiring full text comprehension.
This document discusses cognitive plausibility in learning algorithms, with a focus on natural language processing. It outlines the author's background and motivation, which is to model human learning and communication more accurately. Some key points made include: understanding language acquisition as discriminative learning rather than compositional; explaining features of human language through models like Rescorla-Wagner learning; and how naive discrimination learning can be applied to NLP tasks through an incremental learning algorithm. The document also provides an overview of available NLP tools and limitations in fully achieving language understanding.
This document discusses research on automated text summarization. It defines a summary as a shorter text that retains the key information from the original text(s). There are typically three stages to automated summarization: topic identification to extract important units, interpretation to fuse concepts using external knowledge, and generation to produce coherent readable text. Various methods are reviewed for the topic identification stage, including analyzing positional, cue phrase, frequency-based, title overlap, and discourse structure criteria. Combining the scores from different methods improves performance over using a single method alone.
Information Retrieval using Semantic SimilaritySaswat Padhi
This document summarizes a seminar on artificial intelligence that covered three main topics: information retrieval using semantics and ontology, semantic similarity, and information retrieval. It discusses how semantics and ontologies can help address what information retrieval is currently lacking by providing meaning. It then covers different approaches to measuring semantic similarity based on path lengths and information content in ontologies. Finally, it discusses how information retrieval can be improved by reweighting query terms and expanding queries based on semantic similarity to related terms.
1. The document discusses approaches to discourse analysis (DA), focusing on Conversation Analysis (CA). CA aims to closely analyze talk-in-interaction to describe patterns of communication and social acts.
2. There are two branches of CA - Linguistic CA focuses only on language, while Ethnomethodological CA prioritizes social acts and how language enables them.
3. Ethnomethodological CA follows five stages of analysis: unmotivated looking, inductive search, establishing regularities, detailed analysis, and a generalized account. The goal is to understand social acts from the participants' perspective.
The document discusses research into analyzing different content channels, such as digital ink, speech, and slides, from classroom lectures recorded using a tablet PC. The researchers explored handwriting recognition, the relationship between written and spoken words, identifying attentional marks on slides and their associated content, and recognizing correction activities. The results showed basic handwriting recognition was surprisingly accurate, a strong co-occurrence between written and spoken words, the ability to identify attentional marks and linked content, and potential to recognize some high-level activities like corrections. The research aimed to better understand real presentation data to guide building tools for automatic analysis of educational content channels.
A DECADE OF USING HYBRID INFERENCE SYSTEMS IN NLP (2005 – 2015): A SURVEYijaia
In today’s world of digital media, connecting millions of users, large amounts of information is being
generated. These are potential mines of knowledge and could give deep insights about the trends of both
social and scientific value. However, owing to the fact that most of this is highly unstructured, we cannot
make any sense of it. Natural language processing (NLP) is a serious attempt in this direction to organise
the textual matter which is in a human understandable form (natural language) in a meaningful and
insightful way. In this, text entailment can be considered a key component in verifying or proving the
correctness or efficiency of this organisation. This paper tries to make a survey of various text entailment
methods proposed giving a comparative picture based on certain criteria like robustness and semantic
precision.
Psychological Barriers to Communication arise due to factors within an individual's mind that inhibit effective transmission and reception of messages during communication. Some key psychological barriers include:
- Emotional barriers: Strong emotions like anger, anxiety, stress can distort thinking and prevent listening with an open mind.
- Perceptual barriers: Differences in individual perspectives and experiences lead to biases in how people perceive and interpret messages. People also tend to perceive things based on their own frame of reference without verifying accuracy.
- Selectivity: The human mind can only focus on a limited amount of stimuli at once. During communication, selectivity determines what information receives attention and what gets filtered out, potentially missing important details.
That covers some of the main psychological
Conversation analysis is a research tradition that examines recorded, naturally occurring conversations to understand how participants organize turn-taking and negotiate relationships. It believes interaction determines social dynamics. Researchers analyze transcripts of audio/video recordings without hypotheses, focusing on patterns across contexts. The goal is describing competencies that enable intelligible social interaction. Reports provide context, describe phenomena through examples from data, and interpret underlying organizational patterns.
On Methods for the Formal Specification of Fault Tolerant SystemsMazzara1976
1. The document discusses formal methods for specifying fault tolerant systems, defining them as methods that use mathematics and logic to introduce rigor into the software development process.
2. It proposes a schema for evaluating formal methods that includes having an underlying computational model, a defined language, defined steps and guidance for applying the method.
3. The document outlines a three step method for specifying fault tolerant systems that involves defining system boundaries, deriving specifications, and exposing assumptions about the environment.
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
The document discusses the impact of standardized terminologies and domain ontologies in multilingual information processing. It outlines how natural language processing (NLP) techniques can be used to semi-automatically populate ontologies by extracting information from text. Integrating knowledge from ontologies, NLP tools, and subject experts allows for more effective information access and management in an organization.
This document presents a theoretical framework for analyzing network-based discussion groups online. It discusses Niklas Luhmann's systems theory and Rafaelis's concept of interactivity as theoretical starting points. It proposes analyzing online discussions across three dimensions (semantic, temporal, personal/structural) and four levels (message, topic, person, thread) in a research matrix. Based on an literature review, it hypothesizes patterns of communicative progress in online discussions, such as a high proportion of unanswered messages and an unequal distribution of participant activity.
The net is rife with rumours that spread through microblogs and social media. Not all the claims in these can be verified. However, recent work has shown that the stances alone that commenters take toward claims can be sufficiently good indicators of claim veracity, using e.g. an HMM that takes conversational stance sequences as the only input. Existing results are monolingual (English) and mono-platform (Twitter). This paper introduces a stanceannotated Reddit dataset for the Danish language, and describes various implementations of stance classification models. Of these, a Linear SVM provides predicts stance best, with 0.76 accuracy / 0.42 macro F1. Stance labels are then used to predict veracity across platforms and also across languages, training on conversations held in one language and using the model on conversations held in another. In our experiments, monolinugal scores reach stance-based veracity accuracy of 0.83 (F1 0.68); applying the model across languages predicts veracity of claims with an accuracy of 0.82 (F1 0.67). This demonstrates the surprising and powerful viability of transferring stance-based veracity prediction across languages.
What is the state of natural language processing for Danish in 2018? This reviews language technology in Denmark this year. Present at a "Puzzle of Danish" workshop.
More Related Content
Similar to Determining the Types of Temporal Relations in Discourse
Determining the Types of Temporal Relations in DiscourseLeon Derczynski
This document discusses determining the types of temporal relations in discourse. It introduces key temporal information extraction concepts like events, temporal expressions, and links between events and times. The document also examines relation extraction challenges, the role of temporal signals and tense in modelling temporal relations, and potential areas of future work such as temporal dataset construction.
Automatic temporal ordering of events described in discourse has been of great interest in recent years. Event orderings are
conveyed in text via various linguistic mechanisms including the use of expressions such as “before”, “after” or “during”
that explicitly assert a temporal relation – temporal signals. We investigate the role of temporal signals in temporal relation extraction and provide a quantitative analysis of these expressions in the TimeBank annotated corpus.
This document discusses theories of latent semantics and social interaction. It outlines latent semantic analysis (LSA) and social network analysis (SNA) as methods to analyze meaning and interactions. It proposes meaningful interaction analysis (MIA) as a technique that combines LSA and SNA to study associative closeness structures and social relations in latent semantic spaces. Examples of applying MIA to analyze forum postings, virtual meeting attendance, and blog subscriptions are provided.
Foundations Defining Communication & Communication Study.docxbudbarber38650
Foundations:
Defining Communication &
Communication Study
Survey of Communication Study, Hahn and Paynton, chpt. 1
https://en.wikibooks.org/wiki/Survey_of_Communication_Study/Chapter_1_-_Foundations:_Defining_Communication_and_Communication_Study
Introductions
Discuss the course syllabus, assignments and online Textbook
Blackboard how-to
Communication Definitions
Communication Models
Linear Model
Transactional Model
Communication and You
Agenda
Introductions
Introduce yourself:
Name
Hometown
Year in School
Major
Dream Job
Go over syllabus and Blackboard
3
We will be using a combination of two FREE online textbooks, known as an OER (Open Educational Resources)
These are free online textbooks written by communication faculty who have allowed open use of the material.
“Survey of Communication Study” by Humboldt State University professors Laura K. Hahn and Scott T. Paynton. Last edited online 2016
https://en.wikibooks.org/wiki/Survey_of_Communication_Study
“Communication in the Real World” 2012 Creative Common License, publisher and author name removed per request
http://open.lib.umn.edu/communication/
Textbooks
If you are viewing the PPT slides on your computer or smart phone, view in the slideshow function so you will be able to interact with the various GIFs and video links that are included
Suggestion for viewing PPTs
What is involved in the process of communication?
Why is communication important?
Name people who use communication in creative ways?
Discussion:
What is Communication?
Instructor notes: These question prompts can be used as a pair activity, group activity, class discussion or “thinking point” depending on the class size and modality
6
Memes are an Example of Creative Communication
Without looking at the textbook, write down a one sentence definition of communication
To Do
70 years ago communication scholars Bruce Smith, Harold Lasswell and Ralph D. Casey stated
“Communication study is an academic field whose primary focus is ‘who says what, through what channels (media) of communication, to whom [and] what will be the results’”
(Emphasis and underline added)
Communication Definition
Smith, Lasswell and Casey
The National Communication Association states
“Communication study focuses on how people use messages to generate meaning within and across various contexts, cultures, channels and media.”
“The discipline promotes the effective and ethical practice of human communication.”
Communication Definition
National Communication Association
For the purposes of our class, the definition we will use is
“Communication is the process of using symbols to exchange meaning.”
Communication Definition
Our Textbook
A Model is a visual representation/depiction of how something works.
Models allow us to understand a process by dividing it into parts and looking at how they are related
Models of Communication
The earliest models of comm.
This document provides an overview of natural language processing (NLP) and the use of deep learning for NLP tasks. It discusses how deep learning models can learn representations and patterns from large amounts of unlabeled text data. Deep learning approaches are now achieving superior results to traditional NLP methods on many tasks, such as named entity recognition, machine translation, and question answering. However, deep learning models do not explicitly model linguistic knowledge. The document outlines common NLP tasks and how deep learning algorithms like LSTMs, CNNs, and encoder-decoder models are applied to problems involving text classification, sequence labeling, and language generation.
Semantic Relation Classification: Task Formalisation and RefinementAndre Freitas
The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded,
allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.
The document discusses dynamic systems theory and its application to second language acquisition. It describes key aspects of dynamic systems like dynamism, adaptation, heterogeneity, openness and nonlinearity. These concepts are then related to how a language classroom can be viewed as a dynamic system with interacting elements. The document also discusses factors that influence second language learning like frequency, contingency, salience, multiple cues, and interference from the first language.
The role of linguistic information for shallow language processingConstantin Orasan
The document discusses shallow language processing and summarization. It argues that while deep language understanding is limited, shallow methods can be improved by adding linguistic information. As an example, it shows how term frequency, anaphora resolution, discourse cues and genetic algorithms can select extractive summaries that better match human abstracts, without requiring full text comprehension.
This document discusses cognitive plausibility in learning algorithms, with a focus on natural language processing. It outlines the author's background and motivation, which is to model human learning and communication more accurately. Some key points made include: understanding language acquisition as discriminative learning rather than compositional; explaining features of human language through models like Rescorla-Wagner learning; and how naive discrimination learning can be applied to NLP tasks through an incremental learning algorithm. The document also provides an overview of available NLP tools and limitations in fully achieving language understanding.
This document discusses research on automated text summarization. It defines a summary as a shorter text that retains the key information from the original text(s). There are typically three stages to automated summarization: topic identification to extract important units, interpretation to fuse concepts using external knowledge, and generation to produce coherent readable text. Various methods are reviewed for the topic identification stage, including analyzing positional, cue phrase, frequency-based, title overlap, and discourse structure criteria. Combining the scores from different methods improves performance over using a single method alone.
Information Retrieval using Semantic SimilaritySaswat Padhi
This document summarizes a seminar on artificial intelligence that covered three main topics: information retrieval using semantics and ontology, semantic similarity, and information retrieval. It discusses how semantics and ontologies can help address what information retrieval is currently lacking by providing meaning. It then covers different approaches to measuring semantic similarity based on path lengths and information content in ontologies. Finally, it discusses how information retrieval can be improved by reweighting query terms and expanding queries based on semantic similarity to related terms.
1. The document discusses approaches to discourse analysis (DA), focusing on Conversation Analysis (CA). CA aims to closely analyze talk-in-interaction to describe patterns of communication and social acts.
2. There are two branches of CA - Linguistic CA focuses only on language, while Ethnomethodological CA prioritizes social acts and how language enables them.
3. Ethnomethodological CA follows five stages of analysis: unmotivated looking, inductive search, establishing regularities, detailed analysis, and a generalized account. The goal is to understand social acts from the participants' perspective.
The document discusses research into analyzing different content channels, such as digital ink, speech, and slides, from classroom lectures recorded using a tablet PC. The researchers explored handwriting recognition, the relationship between written and spoken words, identifying attentional marks on slides and their associated content, and recognizing correction activities. The results showed basic handwriting recognition was surprisingly accurate, a strong co-occurrence between written and spoken words, the ability to identify attentional marks and linked content, and potential to recognize some high-level activities like corrections. The research aimed to better understand real presentation data to guide building tools for automatic analysis of educational content channels.
A DECADE OF USING HYBRID INFERENCE SYSTEMS IN NLP (2005 – 2015): A SURVEYijaia
In today’s world of digital media, connecting millions of users, large amounts of information is being
generated. These are potential mines of knowledge and could give deep insights about the trends of both
social and scientific value. However, owing to the fact that most of this is highly unstructured, we cannot
make any sense of it. Natural language processing (NLP) is a serious attempt in this direction to organise
the textual matter which is in a human understandable form (natural language) in a meaningful and
insightful way. In this, text entailment can be considered a key component in verifying or proving the
correctness or efficiency of this organisation. This paper tries to make a survey of various text entailment
methods proposed giving a comparative picture based on certain criteria like robustness and semantic
precision.
Psychological Barriers to Communication arise due to factors within an individual's mind that inhibit effective transmission and reception of messages during communication. Some key psychological barriers include:
- Emotional barriers: Strong emotions like anger, anxiety, stress can distort thinking and prevent listening with an open mind.
- Perceptual barriers: Differences in individual perspectives and experiences lead to biases in how people perceive and interpret messages. People also tend to perceive things based on their own frame of reference without verifying accuracy.
- Selectivity: The human mind can only focus on a limited amount of stimuli at once. During communication, selectivity determines what information receives attention and what gets filtered out, potentially missing important details.
That covers some of the main psychological
Conversation analysis is a research tradition that examines recorded, naturally occurring conversations to understand how participants organize turn-taking and negotiate relationships. It believes interaction determines social dynamics. Researchers analyze transcripts of audio/video recordings without hypotheses, focusing on patterns across contexts. The goal is describing competencies that enable intelligible social interaction. Reports provide context, describe phenomena through examples from data, and interpret underlying organizational patterns.
On Methods for the Formal Specification of Fault Tolerant SystemsMazzara1976
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2. It proposes a schema for evaluating formal methods that includes having an underlying computational model, a defined language, defined steps and guidance for applying the method.
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Similar to Determining the Types of Temporal Relations in Discourse (20)
The net is rife with rumours that spread through microblogs and social media. Not all the claims in these can be verified. However, recent work has shown that the stances alone that commenters take toward claims can be sufficiently good indicators of claim veracity, using e.g. an HMM that takes conversational stance sequences as the only input. Existing results are monolingual (English) and mono-platform (Twitter). This paper introduces a stanceannotated Reddit dataset for the Danish language, and describes various implementations of stance classification models. Of these, a Linear SVM provides predicts stance best, with 0.76 accuracy / 0.42 macro F1. Stance labels are then used to predict veracity across platforms and also across languages, training on conversations held in one language and using the model on conversations held in another. In our experiments, monolinugal scores reach stance-based veracity accuracy of 0.83 (F1 0.68); applying the model across languages predicts veracity of claims with an accuracy of 0.82 (F1 0.67). This demonstrates the surprising and powerful viability of transferring stance-based veracity prediction across languages.
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Efficient named entity annotation through pre-emptingLeon Derczynski
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Video cast: https://www.youtube.com/watch?v=QUbRmUinhHw&feature=youtu.be
Corpus Annotation through Crowdsourcing: Towards Best Practice GuidelinesLeon Derczynski
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Full paper: https://gate.ac.uk/sale/lrec2014/crowdsourcing/crowdsourcing-NLP-corpora.pdf
Passive-Aggressive Sequence Labeling with Discriminative Post-Editing for Rec...Leon Derczynski
Presentation with audio: https://www.youtube.com/watch?v=heYj8sCmWCo
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Full paper: http://derczynski.com/sheffield/papers/person_tweets.pdf
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A PhD course at the University of Szeged, organised by the FuturICT.hu project; 2013. December 9-13.
1. Twitter intro + JSON structure
2. Challenges in analysing social media: why traditional NLP models do not work well
3. GATE for social media
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Recognising and Interpreting Named Temporal ExpressionsLeon Derczynski
Paper: http://derczynski.com/sheffield/papers/named_timex.pdf
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TwitIE: An Open-Source Information Extraction Pipeline for Microblog TextLeon Derczynski
Code: http://gate.ac.uk/wiki/twitie.html
Paper: https://gate.ac.uk/sale/ranlp2013/twitie/twitie-ranlp2013.pdf
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Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy DataLeon Derczynski
Download software: http://gate.ac.uk/wiki/twitter-postagger.html
Original paper: http://derczynski.com/sheffield/papers/twitter_pos.pdf
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Empirical Validation of Reichenbach’s Tense FrameworkLeon Derczynski
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Determining the Types of Temporal Relations in Discourse
1. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Determining the Types of Temporal Relations in
Discourse
Leon Derczynski
University of Sheffield
5 March, 2013
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
2. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
The Role of Time
Why is time important in language processing?
World state changes constantly
Every empirical assertion has temporal bounds
“The sky is blue”, but it was not always
Without it, na¨ıve knowledge extraction will fail (given an
Almanac of Presidents, who is President?)
By understanding temporal information, you will do better
knowledge extraction.
Overall goal
How do we automatically understand temporal information in
natural languages?
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
3. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Temporal Information Extraction
Existing state of the art
How can we categorise types of temporal information?
Events – e.g. occurrences, states
Temporal expressions (timexes) – e.g. dates, durations
Links – relations between pairs of events or times
Supporting texts – e.g. action cardinality, event ordering
We develop and use ISO-TimeML to annotate these entities.
Main dataset: TimeBank (about 180 annotated documents)
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
4. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
TimeML
Organizers
<EVENT eid="e2120" class="REPORTING">state</EVENT>
the
<TIMEX3 tid="t29" type="DURATION" value="P2D"
temporalFunction="false"
functionInDocument="NONE">two days</TIMEX3>
of music, dancing, and speeches is
<EVENT eid="e2123" class="I STATE">expected</EVENT>
to
<EVENT eid="e13" class="OCCURRENCE">draw</EVENT>
some two million people.
<TLINK eventID="e2123" relatedToTime="t29" relType="BEFORE"/>
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
5. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Times and Events
What are temporal expressions?
They refer to a time
Subtasks: recognition and interpretation; SotA recognition is
0.86 F1
What do we consider as events?
Verbal, nominal
State of the art: 0.90 F1 for recognition
Doesn’t cover complex structure; e.g. a music festival
Events are not very useful unless related to other temporal
entities
How can we describe this structural complexity?
Start by modeling the document as a graph
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
6. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Temporal relations
What are temporal relations?
They describe the links between times and events
Can capture both complex and partial orderings
What kinds of temporal relation are there?
1 Interval (before, after, included by, simultaneous)
2 Subordinate (reported speech, modal, conditional)
3 Aspectual (start, culmination – see Vendler, Comrie)
This work is concerned with the coarsest-grained information: the
first category
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
7. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Problem Definition
How are these relations represented?
Temporal interval algebra (Allen 1984) – a set of 14 relations
between a pair of intervals
TimeML defines a set of relation types and also types of
interval
What is our problem?
Assume discourse w/ perfect event and timex annotations
In fact, assume we know which intervals to link!
“Given an ordered pair of intervals (arg1, arg2), which relation in
the set Rallen describes them?”
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
8. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Relation Extraction
How can relations be labelled?
Machine learning
Using TimeML attributes: some success
Using syntactic relations: matches SotA in tree kernels
What’s the state of the art?
2007: Mani et al.: baseline 56%, system has 61% accuracy
2008: Bethard, Chambers: many sophisticated improvements
– ILP, timex-timex ordering. Improved on Mani et al. by 1.5%.
2010: TempEval-2: baseline 58%, best was 65% accuracy
Why do we find this performance ceiling?
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
9. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Sources of Temporal Relation Information
What are we missing?
There is a heterogeneous set of temporal information types,
including:
Explicit signals – subsequently, as soon as
Linguistic theory offers some models
What is the evidence these two types will help?
Conducted failure analysis: TempEval-2010 1
Multiple diverse approaches, same dataset
Find the set of difficult links
Characterise information supporting these links
1
Verhagen et al., 2010: Semeval Task 13 - TempEval-2
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
10. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Task C: event−timex intra−sentence relations
All systems correct 1 fails 2 fail 3 fail 4 fail 5 fail All systems fail
Task D: event−DCT relations
All systems correct 1 fails 2 fail 3 fail 4 fail All systems fail
Task E: main event inter−sentence relations
All systems correct 1 fails 2 fail 3 fail 4 fail 5 fail All systems fail
Task F: event−subordinate intra−sentence relations
All systems correct 1 fails 2 fail 3 fail 4 fail All systems fail
Figure: TempEval-2 relation labelling tasks, showing proportions of
relations according to the number of systems that gave correct labels.
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
11. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
C D E F
Proportion of links within a task that are difficult
Task
%difficult
010203040
The problem is difficult, and there is a consistently-difficult set of
links. Perhaps we are ignoring some critical information.
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
12. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
New sources of ordering information
Next step: manually characterise each “difficult” link.
Attempt to identify what kind of information could be used to
label it.
Sources to investigate
Explicit text – signals “After you pull the pin, throw the grenade”
Sources to investigate
Tensed relations “Having eaten, I left”
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
13. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Temporal Signals
What are these?
In TimeML, they are text annotated as being helpful to a
temporal relation
Used by 12.2% of TimeBank’s relations
Are temporal signals useful?
A resounding yes! 61% → 83% accuracy with simple
features 2
This level of performance on event-event links is above
general state-of-the-art
Existing corpora are under-annotated
2
Derczynski and Gaizauskas, 2010: Using signals for temporal relation
classification
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
14. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Temporal Signal Annotation
How can we automatically annotate temporal signals?
Define signals formally 3
Define a closed class of signals
Re-annotate TimeBank
Train discrimination and association
We included dependency information and function tagging.
3
Derczynski and Gaizauskas, 2011: A corpus based study of temporal signals
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
15. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Results
How well did our approach perform?
1 Discrimination: 92% accuracy, 75% accuracy on positives
(0.77 IAA)
2 Association: 99% accuracy / 80% error reduction
3 Inductive bias towards independence assumption was harmful
(MaxEnt, NBayes)
Results: 16% of links have signals (31% improvement) and can
now be labelled at high accuracy.
What remains to be done?
How can we remedy under-annotation at the source?
Clear links to spatial signal annotation (e.g. -LOC tags)
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
16. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Reichenbach’s Model of Verbs
How can we model tense in language?
Each verb happens at event time, E
The verb is uttered at speech time, S
Past tense: E < S John ran.
Present tense: E = S I’m free!
What differentiates simple past from past perfect?
John ran. is not the same as John had run.
Introduce abstract reference time, R
John had run. E < R < S
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
17. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Reasoning about tense
How is Reichenbach’s model helpful?
We can describe all verbal events as three points linked by
either equality or precedence
Automatic and quick inference for relating intervals
Does it work?
Conducted first corpus-driven validation of the framework
For reporting-type links, we used features based on pairwise
event-time relations
Add one feature representing the Reichenbachian ordering
Classifier reached 59% accuracy (48% MCC baseline) on 9%
of all temporal relations (above SotA)
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
18. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Extending the model
How else can we use the model?
Positional use
Timexes relate to reference points
Only consider cases where the event and time are linguistically
connected
Identify these using dependency parses
Add a feature hinting at the ordering
We reach 75% accuracy from a 67% baseline (above SotA)
Also useful for timex standard transduction 4
4
Derczynski, Llorens and Saquete 2012: Massively increasing TIMEX3
resources
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
19. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Contributions
A large part of the difficult relation set (roughly 60%) is catered
for by these new information sources.
Difficult task, with notable impact
Focus on automatic annotation of temporal relations
Pushed beyond SotA understanding of the problem
Creation of and contribution to language resources – e.g.
ISO-TimeML, RTMML, CAVaT (among others)
.. where could we go next?
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
20. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Future
Forensic analysis
How can we build a consistent event model from multiple
semi-reliable accounts of an event?
Challenges:
Multi-document event and actor co-reference
Story conflict resolution 5
Spatial and temporal IE from colloquial text
Building and resolving accurate co-constraining models from
unreliable data (belief networks)
5
Regneri, Koller and Pinkal 2010: Learning Script Knowledge with Web
Experiments
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
21. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Future
Assertion bounding
All assertions have temporal bounds. How can we determine these?
Challenges:
Accurate extraction of document temporal structure
Automated reasoning
High-precision timex normalisation
Doing temporal IE & IR at gigaword scale
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
22. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Future
Temporal dataset construction
Many current systems index whole documents by date, but
information is more nuanced than that
Challenges:
Mapping events to temporal data points
Storing and extracting events
Anchoring events with uncertain bounds (“last year’s fighting”
vs. “the fighting on April 23, 2011”)
Mining complex super-events; e.g. the Fukushima disaster;
what happened when?
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
23. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Recap
Temporality is ubiquitous, in the world around us and in the
language we use to describe our world
Processing it automatically is difficult
Doing high-performance temporal IE opens exciting research
avenues
Thank you for your time. Are there any questions?
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
24. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Labellings as probability distributions
Automated methods (e.g. classifiers) may have varying degrees of
confidence about a link’s label.
We could assign a set of labels and probabilities to each label.
Consistency constraints allow us to find the most-likely possible
graph.
A:B → before: 0.9; after 0.1
B:C → before: 0.5; simultaneous: 0.5
A:C → before: 1.0
Very time-consuming to compute
– optimisations welcome!
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse
25. Introduction Concepts and tools Relation Extraction Temporal Signals Modelling Tense Conclusion
Unuttered temporal orderings
Event/Time distance
“When I was brushing my teeth”
→ This event happens at least twice daily; assume this instance is
0-16 hours away
Complex events
“When we were putting up the tents for the festival”
→ near the beginning of / just before the “festival” event
Leon Derczynski University of Sheffield
Determining the Types of Temporal Relations in Discourse