7,325 research outputs found
Extracting causation knowledge from natural language texts.
Chan Ki, Cecia.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 95-99).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Our Contributions --- p.4Chapter 1.2 --- Thesis Organization --- p.5Chapter 2 --- Related Work --- p.6Chapter 2.1 --- Using Knowledge-based Inferences --- p.7Chapter 2.2 --- Using Linguistic Techniques --- p.8Chapter 2.2.1 --- Using Linguistic Clues --- p.8Chapter 2.2.2 --- Using Graphical Patterns --- p.9Chapter 2.2.3 --- Using Lexicon-syntactic Patterns of Causative Verbs --- p.10Chapter 2.2.4 --- Comparisons with Our Approach --- p.10Chapter 2.3 --- Discovery of Extraction Patterns for Extracting Relations --- p.11Chapter 2.3.1 --- Snowball system --- p.12Chapter 2.3.2 --- DIRT system --- p.12Chapter 2.3.3 --- Comparisons with Our Approach --- p.13Chapter 3 --- Semantic Expectation-based Knowledge Extraction --- p.14Chapter 3.1 --- Semantic Expectations --- p.14Chapter 3.2 --- Semantic Template --- p.16Chapter 3.2.1 --- Causation Semantic Template --- p.16Chapter 3.3 --- Sentence Templates --- p.17Chapter 3.4 --- Consequence and Reason Templates --- p.22Chapter 3.5 --- Causation Knowledge Extraction Framework --- p.25Chapter 3.5.1 --- Template Design --- p.25Chapter 3.5.2 --- Sentence Screening --- p.27Chapter 3.5.3 --- Semantic Processing --- p.28Chapter 4 --- Using Thesaurus and Pattern Discovery for SEKE --- p.33Chapter 4.1 --- Using a Thesaurus --- p.34Chapter 4.2 --- Pattern Discovery --- p.37Chapter 4.2.1 --- Use of Semantic Expectation-based Knowledge Extraction --- p.37Chapter 4.2.2 --- Use of Part of Speech Information --- p.39Chapter 4.2.3 --- Pattern Representation --- p.39Chapter 4.2.4 --- Constructing the Patterns --- p.40Chapter 4.2.5 --- Merging the Patterns --- p.43Chapter 4.3 --- Pattern Matching --- p.44Chapter 4.3.1 --- Matching Score --- p.46Chapter 4.3.2 --- Support of Patterns --- p.48Chapter 4.3.3 --- Relevancy of Sentence Templates --- p.48Chapter 4.4 --- Applying the Newly Discovered Patterns --- p.49Chapter 5 --- Applying SEKE on Hong Kong Stock Market Domain --- p.52Chapter 5.1 --- Template Design --- p.53Chapter 5.1.1 --- Semantic Templates --- p.53Chapter 5.1.2 --- Sentence Templates --- p.53Chapter 5.1.3 --- Consequence and Reason Templates: --- p.55Chapter 5.2 --- Pattern Discovery --- p.58Chapter 5.2.1 --- Support of Patterns --- p.58Chapter 5.2.2 --- Relevancy of Sentence Templates --- p.58Chapter 5.3 --- Causation Knowledge Extraction Result --- p.58Chapter 5.3.1 --- Evaluation Approach --- p.61Chapter 5.3.2 --- Parameter Investigations --- p.61Chapter 5.3.3 --- Experimental Results --- p.65Chapter 5.3.4 --- Knowledge Discovered --- p.68Chapter 5.3.5 --- Parameter Effect --- p.75Chapter 6 --- Applying SEKE on Global Warming Domain --- p.80Chapter 6.1 --- Template Design --- p.80Chapter 6.1.1 --- Semantic Templates --- p.81Chapter 6.1.2 --- Sentence Templates --- p.81Chapter 6.1.3 --- Consequence and Reason Templates --- p.83Chapter 6.2 --- Pattern Discovery --- p.85Chapter 6.2.1 --- Support of Patterns --- p.85Chapter 6.2.2 --- Relevancy of Sentence Templates --- p.85Chapter 6.3 --- Global Warming Domain Result --- p.85Chapter 6.3.1 --- Evaluation Approach --- p.85Chapter 6.3.2 --- Experimental Results --- p.88Chapter 6.3.3 --- Knowledge Discovered --- p.89Chapter 7 --- Conclusions and Future Directions --- p.92Chapter 7.1 --- Conclusions --- p.92Chapter 7.2 --- Future Directions --- p.93Bibliography --- p.95Chapter A --- Penn Treebank Part of Speech Tags --- p.10
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
Extracting causal relationships from Chinese written text
Expert systems form one of the most important research areas in Artificial Intelligence. The main parts in expert systems are knowledge bases and inference engines. In the knowledge bases the main knowledge is knowledge in the form of ``IF-THEN" statements. In knowledge graphs, a new form of knowledge representation, the ``IF-THEN" statements are tied up with causal operators (CAU-relations). In this paper, we picked out some Chinese operators with ``CAU" meaning, and investigated these operators. We also show by an example how to extract causal relations from a given Chinese writing text
Learning causality for Arabic - proclitics
The use of prefixed particles is a prevalent linguistic form to express causation in Arabic Language. However, such particles are complicated and highly ambiguous as they imply different meanings according to their position in the text. This ambiguity emphasizes the high demand for a large-scale annotated corpus that contains instances of these particles. In this paper, we present the process of building our corpus, which includes a collection of annotated sentences each containing an instance of a candidate causal particle. We use the corpus to construct and optimize predictive models for the task of causation recognition. The performance of the best models is significantly better than the baselines. Arabic is a less-resourced language and we hope this work would help in building better Information Extraction systems
The contribution of cause-effect link to representing the core of scientific paper—The role of Semantic Link Network
The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing
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