Taxonomy Induction using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary

Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process

Hans Hjelm. Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process. NEALT Monograph Series, Vol. 1 (2009), 159 pages. © 2009 Hans Hjelm. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/10126

Review of Semantic Importance and Role of using Ontologies in Web Information Retrieval Techniques

The Web contains an enormous amount of information, which is managed to accumulate, researched, and regularly used by many users. The nature of the Web is multilingual and growing very fast with its diverse nature of data including unstructured or semi-structured data such as Websites, texts, journals, and files. Obtaining critical relevant data from such vast data with its diverse nature has been a monotonous and challenging task. Simple key phrase data gathering systems rely heavily on statistics, resulting in a word incompatibility problem related to a specific word's inescapable semantic and situation variants. As a result, there is an urgent need to arrange such colossal data systematically to find out the relevant information that can be quickly analyzed and fulfill the users' needs in the relevant context. Over the years ontologies are widely used in the semantic Web to contain unorganized information systematic and structured manner. Still, they have also significantly enhanced the efficiency of various information recovery approaches. Ontological information gathering systems recover files focused on the semantic relation of the search request and the searchable information. This paper examines contemporary ontology-based information extraction techniques for texts, interactive media, and multilingual data types. Moreover, the study tried to compare and classify the most significant developments utilized in the search and retrieval techniques and their major disadvantages and benefits

Semantic vector representations of senses, concepts and entities and their applications in natural language processing

Representation learning lies at the core of Artificial Intelligence (AI) and Natural Language Processing (NLP). Most recent research has focused on develop representations at the word level. In particular, the representation of words in a vector space has been viewed as one of the most important successes of lexical semantics and NLP in recent years. The generalization power and flexibility of these representations have enabled their integration into a wide variety of text-based applications, where they have proved extremely beneficial. However, these representations are hampered by an important limitation, as they are unable to model different meanings of the same word. In order to deal with this issue, in this thesis we analyze and develop flexible semantic representations of meanings, i.e. senses, concepts and entities. This finer distinction enables us to model semantic information at a deeper level, which in turn is essential for dealing with ambiguity. In addition, we view these (vector) representations as a connecting bridge between lexical resources and textual data, encoding knowledge from both sources. We argue that these sense-level representations, similarly to the importance of word embeddings, constitute a first step for seamlessly integrating explicit knowledge into NLP applications, while focusing on the deeper sense level. Its use does not only aim at solving the inherent lexical ambiguity of language, but also represents a first step to the integration of background knowledge into NLP applications. Multilinguality is another key feature of these representations, as we explore the construction language-independent and multilingual techniques that can be applied to arbitrary languages, and also across languages. We propose simple unsupervised and supervised frameworks which make use of these vector representations for word sense disambiguation, a key application in natural language understanding, and other downstream applications such as text categorization and sentiment analysis. Given the nature of the vectors, we also investigate their effectiveness for improving and enriching knowledge bases, by reducing the sense granularity of their sense inventories and extending them with domain labels, hypernyms and collocations

LiDom builder: Automatising the construction of multilingual domain modules

136 p.Laburpena Lan honetan LiDOM Builder tresnaren analisi, diseinu eta ebaluazioa aurkezten dira. Teknologian oinarritutako hezkuntzarako tresnen Domeinu Modulu Eleaniztunak testuliburu elektronikoetatik era automatikoan erauztea ahalbidetzen du LiDOM Builderek. Ezagutza eskuratzeko, Hizkuntzaren Prozesamendurako eta Ikaste Automatikorako teknikekin batera, hainbat baliabide eleaniztun erabiltzen ditu, besteak beste, Wikipedia eta WordNet.Domeinu Modulu Elebakarretik Domeinu Modulu Eleaniztunerako bidean, LiDOM Builder tresna DOM-Sortze ingurunearen (Larrañaga, 2012; Larrañaga et al., 2014) bilakaera dela esan genezake. Horretarako, LiDOM Builderek domeinua ikuspegi eleaniztun batetik adieraztea ahalbidetzen duen mekanismoa dakar. Domeinu Modulu Eleaniztunak bi maila ezberdinetako ezagutza jasotzen du: Ikaste Domeinuaren Ontologia (IDO), non hizkuntza ezberdinetan etiketatutako topikoak eta hauen arteko erlazio pedagogikoak jasotzen baitira, eta Ikaste Objektuak (IO), hau da, metadatuekin etiketatutako baliabide didaktikoen bilduma, hizkuntza horietan. LiDOM Builderek onartutako hizkuntza guztietan domeinuaren topikoak adierazteko aukera ematen du. Topiko bakoitza lotuta dago dagokion hizkuntzako bere etiketa baliokidearekin. Gainera, IOak deskribatzeko metadatu aberastuak erabiltzen ditu hizkuntza desberdinetan parekideak diren baliabide didaktikoak lotzeko.LiDOM Builderen, hasiera batean, domeinu-modulua hizkuntza jakin batean idatzitako dokumentu batetik erauziko da eta, baliabide eleaniztunak erabiliko dira, gerora, bai topikoak bai IOak beste hizkuntzetan ere lortzeko. Lan honetan, Ingelesez idatzitako liburuek osatuko dute informazio-iturri nagusia bai doitze-prozesuan bai ebaluazio-prozesuan. Zehazki, honako testuliburu hauek erabili dira: Principles of Object Oriented Programming (Wong and Nguyen, 2010), Introduction to Astronomy (Morison, 2008) eta Introduction to Molecular Biology (Raineri, 2010). Baliabide eleaniztunei dagokienez, Wikipedia, WordNet eta Wikipediatik erauzitako beste hainbat ezagutza-base erabili dira. Testuliburuetatik Domeinu Modulu Eleaniztunak eraikitzeko, LiDOM Builder hiru modulu nagusitan oinarritzen da: LiTeWi eta LiReWi moduluak IDO eleaniztuna eraikitzeaz arduratuko dira eta LiLoWi, aldiz, IO eleaniztunak eraikitzeaz. Jarraian, aipatutako modulu bakoitza xehetasun gehiagorekin azaltzen da.¿ LiTeWi (Conde et al., 2015) moduluak, edozein ikaste-domeinutako testuliburu batetik abiatuta, Hezkuntzarako Ontologia bati dagozkion hainbat termino eleaniztun identifikatuko ditu, hala nola TF-IDF, KP-Miner, CValue eta Shallow Parsing Grammar. Hori lortzeko, gainbegiratu gabeko datu-erauzketa teknikez eta Wikipediaz baliatzen da. Ontologiako topikoak erauzteak LiTeWi-n hiru urrats ditu: lehenik hautagai diren terminoen erauzketa; bigarrenik, lortutako terminoen konbinatzea eta fintzea azken termino zerrenda osatuz; eta azkenik, zerrendako terminoak beste hizkuntzetara mapatzea Wikipedia baliatuz.¿ LiReWi (Conde et al., onartzeko) moduluak Hezkuntzarako Ontologia erlazio pedagogikoez aberastuko du, beti ere testuliburua abiapuntu gisa erabilita. Lau motatako erlazio pedagogikoak erauziko ditu (isA, partOf, prerequisite eta pedagogicallyClose) hainbat teknika eta ezagutza-base konbinatuz. Ezagutza-baseen artean Wikipedia, WordNet, WikiTaxonomy, WibiTaxonomy eta WikiRelations daude. LiReWi-k ere hiru urrats emango ditu erlazioak lortzeko: hasteko, ontologiako topikoak erlazioak erauzteko erabiliko diren ezagutza-base desberdinekin mapatuko ditu; gero, hainbat erlazio-erauzle, bakoitza teknika desberdin batean oinarritzen dena, exekutatuko ditu konkurrenteki erlazio hautagaiak erauzteko; eta, bukatzeko, lortutako emaitza guztiak konbinatu eta iragaziko ditu erlazio pedagogikoen azken multzoa lortuz. Gainera, DOM-Sortzetik LiDOM Buildererako trantsizioan, tesi honetan hobetu egin dira dokumentuen indizeetatik erauzitako isA eta partOf erlazioak, Wikipedia baliabide gehigarri bezala erabilita (Conde et al., 2014).¿ LiLoWi moduluak IOak -batzuk eleaniztunak- erauziko ditu, abiapuntuko testuliburutik ez ezik Wikipedia edo WordNet bezalako ezagutza-baseetatik ere. IDO ontologiako topiko bakoitza Wikipedia eta WordNet-ekin mapatu ostean, LiLoWi-k baliabide didaktikoak erauziko ditu hainbat IO erauzlez baliatuz.IO erauzketa-prozesuan, DOM-Sortzetik LiDOM Buildereko bidean, eta Wikipedia eta WordNet erabili aurretik, ingelesa hizkuntza ere gehitu eta ebaluatu da (Conde et al., 2012).LiDOM Builderen ebaluaziori dagokionez, modulu bakoitza bere aldetik testatua eta ebaluatua izan da bai Gold-standard teknika bai aditu-ebaluazioa baliatuz. Gainera, Wikipedia eta WordNet ezagutza-baseen integrazioak IOen erauzketari ekarri dion hobekuntza ere ebaluatu da. Esan genezake kasu guztietan lortu diren emaitzak oso onak direla.Bukatzeko, eta laburpen gisa, lau dira LiDOM Builderek Domeinu Modulu Eleaniztunaren arloari egin dizkion ekarpen nagusiak:¿ Domeinu Modulu Eleaniztunak adierazteko mekanismo egokia.¿ LiTeWiren garapena. Testuliburuetatik Hezkuntzarako Ontologietarako terminologia eleaniztuna erauztea ahalbidetzen du modulu honek. Ingelesa eta Gaztelera hizkuntzentzako termino-erauzlea eskura dago https://github.com/Neuw84/LiTe URLan.¿ LiReWiren garapena. Testuliburuetatik Hezkuntzarako Ontologietarako erlazio pedagogikoak erauztea ahalbidetzen du modulu honek. Erabiltzen duen Wikipedia/WordNet mapatzailea eskura dago https://github.com/Neuw84/Wikipedia2WordNet URLan.¿ LiLoWiren garapena. Testuliburua eta Wikipedia eta WordNet ezagutza-baseak erabilita IO eleaniztunak erauztea ahalbidetzen du modulu honek

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field