12 research outputs found
Knowledge-based methods for automatic extraction of domain-specific ontologies
Semantic web technology aims at developing methodologies for representing large amount of knowledge in web accessible form. The semantics of knowledge should be easy to interpret and understand by computer programs, so that sharing and utilizing knowledge across the Web would be possible. Domain specific ontologies form the basis for knowledge representation in the semantic web. Research on automated development of ontologies from texts has become increasingly important because manual construction of ontologies is labor intensive and costly, and, at the same time, large amount of texts for individual domains is already available in electronic form. However, automatic extraction of domain specific ontologies is challenging due to the unstructured nature of texts and inherent semantic ambiguities in natural language. Moreover, the large size of texts to be processed renders full-fledged natural language processing methods infeasible. In this dissertation, we develop a set of knowledge-based techniques for automatic extraction of ontological components (concepts, taxonomic and non-taxonomic relations) from domain texts. The proposed methods combine information retrieval metrics, lexical knowledge-base(like WordNet), machine learning techniques, heuristics, and statistical approaches to meet the challenge of the task. These methods are domain-independent and automatic approaches. For extraction of concepts, the proposed WNSCA+{PE, POP} method utilizes the lexical knowledge base WordNet to improve precision and recall over the traditional information retrieval metrics. A WordNet-based approach, the compound term heuristic, and a supervised learning approach are developed for taxonomy extraction. We also developed a weighted word-sense disambiguation method for use with the WordNet-based approach. An unsupervised approach using log-likelihood ratios is proposed for extracting non-taxonomic relations. Further more, a supervised approach is investigated to learn the semantic constraints for identifying relations from prepositional phrases. The proposed methods are validated by experiments with the Electronic Voting and the Tender Offers, Mergers, and Acquisitions domain corpus. Experimental results and comparisons with some existing approaches clearly indicate the superiority of our methods. In summary, a good combination of information retrieval, lexical knowledge base, statistics and machine learning methods in this study has led to the techniques efficient and effective for extracting ontological components automatically
Arabic named entity recognition
En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar
las mejores tecnicas para construir un Reconocedor de Entidades Nombradas
en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades
nombradas que se encuentran en un texto arabe de dominio abierto.
La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de
Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la
Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados
gracias al enriquecimiento que a~nade al texto. En la literatura existen diversos trabajos
que investigan la tarea de REN para un idioma especifico o desde una perspectiva
independiente del lenguaje. Sin embargo, hasta el momento, se han publicado muy
pocos trabajos que estudien dicha tarea para el arabe.
El arabe tiene una ortografia especial y una morfologia compleja, estos aspectos aportan
nuevos desafios para la investigacion en la tarea de REN. Una investigacion completa
del REN para elarabe no solo aportaria las tecnicas necesarias para conseguir
un alto rendimiento, sino que tambien proporcionara un analisis de los errores y una
discusion sobre los resultados que benefician a la comunidad de investigadores del
REN. El objetivo principal de esta tesis es satisfacer esa necesidad. Para ello hemos:
1. Elaborado un estudio de los diferentes aspectos del arabe relacionados con dicha
tarea;
2. Analizado el estado del arte del REN;
3. Llevado a cabo una comparativa de los resultados obtenidos por diferentes
tecnicas de aprendizaje automatico;
4. Desarrollado un metodo basado en la combinacion de diferentes clasificadores,
donde cada clasificador trata con una sola clase de entidades nombradas y emplea
el conjunto de caracteristicas y la tecnica de aprendizaje automatico mas
adecuados para la clase de entidades nombradas en cuestion.
Nuestros experimentos han sido evaluados sobre nueve conjuntos de test.Benajiba, Y. (2009). Arabic named entity recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8318Palanci
A comparative study of Chinese and European Internet companies' privacy policy based on knowledge graph
Privacy policy is not only a means of industry self-discipline, but also a way for users to protect their online privacy. The European Union (EU) promulgated the General Data Protection Regulation (GDPR) on May 25th, 2018, while China has no explicit personal data protection law. Based on knowledge graph, this thesis makes a comparative analysis of the Chinese and European Internet companiesâ privacy policies, and combines with the relevant provisions of GDPR, puts forward suggestions on the privacy policy of Internet companies, so as to solve the problem of personal in-formation protection to a certain extent.
Firstly, this thesis chooses the process and methods of knowledge graph construction and analysis. The process of constructing and analyzing the knowledge graph is: data preprocessing, entity extraction, storage in graph database and query. Data preprocessing includes word segmentation and part-of-speech tagging, as well as text format adjustment. Entity extraction is the core of knowledge graph construction in this thesis. Based on the principle of Conditional Random Fields (CRF), CFR++ toolkit is used for the entity extraction. Subsequently, the extracted entities are transformed into â.csvâ format and stored in the graph database Neo4j, so the knowledge graph is generated. Cypher query statements can be used to query information in the graph database.
The next part is about comparison and analysis of the Internet companiesâ privacy policies in China and Europe. After sampling, the overall characteristics of the privacy policies of Chinese and European Internet companies are compared. According to the process of constructing knowledge graphs mentioned above, the âcollected informationâ and âcontact usâ parts of the privacy policy are used to construct the knowledge graphs.
Finally, combined with the relevant content of GDPR, the results of the comparative analysis are further discussed, and suggestions are proposed. Although Chinese Internet companiesâ privacy policies have some merits, they are far inferior to those of European Internet companies. China also needs to enact a personal data protection law according to its national conditions.
This thesis applies knowledge graph to the privacy policy research, and analyses Internet companiesâ privacy policies from a comparative perspective. It also discusses the comparative results with GDPR and puts forward suggestions, and provides reference for the formulation of China's personal information protection law
Improved cross-language information retrieval via disambiguation and vocabulary discovery
Cross-lingual information retrieval (CLIR) allows people to find documents irrespective of the language used in the query or document. This thesis is concerned with the development of techniques to improve the effectiveness of Chinese-English CLIR. In Chinese-English CLIR, the accuracy of dictionary-based query translation is limited by two major factors: translation ambiguity and the presence of out-of-vocabulary (OOV) terms. We explore alternative methods for translation disambiguation, and demonstrate new techniques based on a Markov model and the use of web documents as a corpus to provide context for disambiguation. This simple disambiguation technique has proved to be extremely robust and successful. Queries that seek topical information typically contain OOV terms that may not be found in a translation dictionary, leading to inappropriate translations and consequent poor retrieval performance. Our novel OOV term translation method is based on the Chinese authorial practice of including unfamiliar English terms in both languages. It automatically extracts correct translations from the web and can be applied to both Chinese-English and English-Chinese CLIR. Our OOV translation technique does not rely on prior segmentation and is thus free from seg mentation error. It leads to a significant improvement in CLIR effectiveness and can also be used to improve Chinese segmentation accuracy. Good quality translation resources, especially bilingual dictionaries, are valuable resources for effective CLIR. We developed a system to facilitate construction of a large-scale translation lexicon of Chinese-English OOV terms using the web. Experimental results show that this method is reliable and of practical use in query translation. In addition, parallel corpora provide a rich source of translation information. We have also developed a system that uses multiple features to identify parallel texts via a k-nearest-neighbour classifier, to automatically collect high quality parallel Chinese-English corpora from the web. These two automatic web mining systems are highly reliable and easy to deploy. In this research, we provided new ways to acquire linguistic resources using multilingual content on the web. These linguistic resources not only improve the efficiency and effectiveness of Chinese-English cross-language web retrieval; but also have wider applications than CLIR
Constructing and modeling text-rich information networks: a phrase mining-based approach
A lot of digital ink has been spilled on "big data" over the past few years, which is often characterized by an explosion of information. Most of this surge owes its origin to the unstructured data in the wild like words, images and video as comparing to the structured information stored in fielded form in databases. The proliferation of text-heavy data is particularly overwhelming, reflected in everyone's daily life in forms of web documents, business reviews, news, social posts, etc. In the mean time, textual data and structured entities often come in intertwined, such as authors/posters, document categories and tags, and document-associated geo locations. With this background, a core research challenge presents itself as how to turn massive, (semi-)unstructured data into structured knowledge. One promising paradigm studied in this dissertation is to integrate structured and unstructured data, constructing an organized heterogeneous information network, and developing powerful modeling mechanisms on such organized network. We name it text-rich information network, since it is an integrated representation of both structured and unstructured textual data.
To thoroughly develop the construction and modeling paradigm, this dissertation will focus on forming a scalable data-driven framework and propose a new line of techniques relying on the idea of phrase mining to bridge textual documents and structured entities.
We will first introduce the phrase mining method named SegPhrase+ to globally discover semantically meaningful phrases from massive textual data, providing a high quality dictionary for text structuralization. Clearly distinct from previous works that mostly focused on raw statistics of string matching, SegPhrase+ looks into the phrase context and effectively rectifies raw statistics to significantly boost the performance.
Next, a novel algorithm based on latent keyphrases is developed and adopted to largely eliminate irregularities in massive text via providing an consistent and interpretable document representation. As a critical process in constructing the network, it uses the quality phrases generated in the previous step as candidates. From them a set of keyphrases are extracted to represent a particular document with inferred strength through a statistical model. After this step, documents become more structured and are consistently represented in the form of a bipartite network connecting documents with quality keyphrases. A more heterogeneous text-rich information network can be constructed by incorporating different types of document-associated entities as additional nodes.
Lastly, a general and scalable framework, Tensor2vec, are to be added to trational data minining machanism, as the latter cannot readily solve the problem when the organized heterogeneous network has nodes with different types. Tensor2vec is expected to elegantly handle relevance search, entity classification, summarization and recommendation problems, by making use of higher-order link information and projecting multi-typed nodes into a shared low-dimensional vectorial space such that node proximity can be easily computed and accurately predicted
Transition-based combinatory categorial grammar parsing for English and Hindi
Given a natural language sentence, parsing is the task of assigning it a grammatical
structure, according to the rules within a particular grammar formalism. Different
grammar formalisms like Dependency Grammar, Phrase Structure Grammar, Combinatory
Categorial Grammar, Tree Adjoining Grammar are explored in the literature for
parsing. For example, given a sentence like âJohn ate an appleâ, parsers based on the
widely used dependency grammars find grammatical relations, such as that âJohnâ is
the subject and âappleâ is the object of the action âateâ. We mainly focus on Combinatory
Categorial Grammar (CCG) in this thesis.
In this thesis, we present an incremental algorithm for parsing CCG for two diverse
languages: English and Hindi. English is a fixed word order, SVO (Subject-Verb-
Object), and morphologically simple language, whereas, Hindi, though predominantly
a SOV (Subject-Object-Verb) language, is a free word order and morphologically rich
language. Developing an incremental parser for Hindi is really challenging since the
predicate needed to resolve dependencies comes at the end. As previously available
shift-reduce CCG parsers use English CCGbank derivations which are mostly right
branching and non-incremental, we design our algorithm based on the dependencies
resolved rather than the derivation. Our novel algorithm builds a dependency graph in
parallel to the CCG derivation which is used for revealing the unbuilt structure without
backtracking. Though we use dependencies for meaning representation and CCG for
parsing, our revealing technique can be applied to other meaning representations like
lambda expressions and for non-CCG parsing like phrase structure parsing.
Any statistical parser requires three major modules: data, parsing algorithm and
learning algorithm. This thesis is broadly divided into three parts each dealing with
one major module of the statistical parser. In Part I, we design a novel algorithm
for converting dependency treebank to CCGbank. We create Hindi CCGbank with a
decent coverage of 96% using this algorithm. We also do a cross-formalism experiment
where we show that CCG supertags can improve widely used dependency parsers.
We experiment with two popular dependency parsers (Malt and MST) for two diverse
languages: English and Hindi. For both languages, CCG categories improve the overall
accuracy of both parsers by around 0.3-0.5% in all experiments. For both parsers,
we see larger improvements specifically on dependencies at which they are known
to be weak: long distance dependencies for Malt, and verbal arguments for MST.
The result is particularly interesting in the case of the fast greedy parser (Malt), since
improving its accuracy without significantly compromising speed is relevant for large
scale applications such as parsing the web.
We present a novel algorithm for incremental transition-based CCG parsing for
English and Hindi, in Part II. Incremental parsers have potential advantages for applications
like language modeling for machine translation and speech recognition. We
introduce two new actions in the shift-reduce paradigm for revealing the required information
during parsing. We also analyze the impact of a beam and look-ahead for
parsing. In general, using a beam and/or look-ahead gives better results than not using
them. We also show that the incremental CCG parser is more useful than a non-incremental
version for predicting relative sentence complexity. Given a pair of sentences
from wikipedia and simple wikipedia, we build a classifier which predicts if one
sentence is simpler/complex than the other. We show that features from a CCG parser
in general and incremental CCG parser in particular are more useful than a chart-based
phrase structure parser both in terms of speed and accuracy.
In Part III, we develop the first neural network based training algorithm for parsing
CCG. We also study the impact of neural network based tagging models, and greedy
versus beam-search parsing, by using a structured neural network model. In greedy
settings, neural network models give significantly better results than the perceptron
models and are also over three times faster. Using a narrow beam, structured neural
network model gives consistently better results than the basic neural network model.
For English, structured neural network gives similar performance to structured perceptron
parser. But for Hindi, structured perceptron is still the winner
Ontologie-basierte Monosemierung - Bestimmung von Referenzen im SemanticWeb
Die vorliegende Arbeit beschäftigt sich mit dieser Thematik, insbesondere mit der Problematik der Ambiguität, die bei der Zusammenfßhrung natßrlich-sprachlicher Informationen mit dem durch Ontologien repräsentierten Wissen auftritt
Ontologie-basierte Monosemierung
Ontologien verlangen eine eindeutige Identifikation der darin beschriebenen Elemente. Mit der Einbindung natßrlicher Sprache erhält auch die Thematik der Mehrdeutigkeit Einzug in die formal geordnete Darstellung. Eine eindeutige Suche anhand natßrlicher Sprache erscheint daher zunächst als unmÜglich. Der Fokus dieser Arbeit liegt auf der LÜsung des Problems der Ambiguität, die bei der Zusammenfßhrung natßrlich-sprachlicher Informationen mit dem durch Ontologien repräsentieren Wissen auftritt