Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web

Search engines are an indispensable part of a web user's life. A vast majority of these web users experience difficulties caused by the keyword-based search engines such as inaccurate results for queries and irrelevant URLs even though the given keyword is present in them. Also, relevant URLs may be lost as they may have the synonym of the keyword and not the original one. This condition is known as the polysemy problem. To alleviate these problems, we propose an algorithm called automatic discovery and ranking of synonyms for search keywords in the web (ADRS). The proposed method generates a list of candidate synonyms for individual keywords by employing the relevance factor of the URLs associated with the synonyms. Then, ranking of these candidate synonyms is done using co-occurrence frequencies and various page count-based measures. One of the major advantages of our algorithm is that it is highly scalable which makes it applicable to online data on the dynamic, domain-independent and unstructured World Wide Web. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard

エンティティ・リンキングのための候補検索とランキング方法に関する研究

Tohoku University乾健太郎課

Entity Linking to Wikipedia : Grounding entity mentions in natural language text using thematic context distance and collective search

This thesis proposes new methods for entity linking in natural language text that assigns entity mentions in unstructured natural language text to the semi-structured encyclopedia Wikipedia. Doing so, entity linking grounds a mention to an encyclopedic entry in Wikipedia and embeds it into this Linked-Open-Data hub. This enables a higher level view on single documents, provides hints for further reading and may be used to add details from other sources. Furthermore, enriching text documents with such links simultaneously resolves the ambiguity of entity names. This ambiguity is an unsolved challenge for many text mining applications: one entity may be designated by a multitude of names and every mention may denote a multitude of entities. Resolving the ambiguity of entity names is thus a crucial step for entity based retrieval, an open problem for most information retrieval and extraction tasks. For instance, search engines relying on heuristic string matches often retrieve irrelevant results as they can not satisfyingly resolve ambiguity. Moreover, there is a huge number of entity mentions that can not be linked to Wikipedia since albeit of its size, Wikipedia has a restricted coverage. Earlier and current work often ignored this and consequently all mentions of uncovered entities. Other approaches handle only entity mentions of specific types or are focussed on English as target language. Apart from such restrictions, no method achieves perfect linking performance. These are the tasks approached in this thesis. We introduce new methods for candidate entity retrieval and candidate entity consolidation, the key components to recall and precision, exploiting both the vast amount of structured and unstructured information stored in Wikipedia. First, we propose a new contextual similarity measure based on latent topic distributions inferred from unstructured natural language text. We show that this thematic distance between mention and candidate entity contexts yields a lower linking error rate than purely word based distances. Being language independent, this method enables high performance entity linking in previously neglected languages such as German and French. This approach is especially suitable, albeit not restricted to link person names, the class of mentions with highest ambiguity. We next propose a new candidate retrieval method to enable successful entity linking also for other entities that are not referenced canonically or exhibit the thematic coherence of persons. We introduce collective search that uses the structured information encoded in Wikipedia’s hyperlink graph to arrive at sets of strongly related candidate entities. This enables us to better handle synonymy, one of the hardest problems in entity linking and not thoroughly treated in previous work. We emphasize on general applicability and evaluate this method on a broad collection of benchmark corpora both in a supervised as well as in an unsupervised setting. We show that candidate enhancement through collective search increases linking performance on nearly all of these corpora and that our method is the most stable compared to other state-of-the-art approaches. Presenting the first unification of diverse performance measures, we also make a step forward to the comparability of entity linking methods. In conclusion, we provide state-of-the-art entity linking methods for nearly all of the current use cases. When it comes to fine-tuning, we note that entity linking has subjective aspects and adaptions may be necessary depending on the task at hand

Linking named entities to Wikipedia

Natural language is fraught with problems of ambiguity, including name reference. A name in text can refer to multiple entities just as an entity can be known by different names. This thesis examines how a mention in text can be linked to an external knowledge base (KB), in our case, Wikipedia. The named entity linking (NEL) task requires systems to identify the KB entry, or Wikipedia article, that a mention refers to; or, if the KB does not contain the correct entry, return NIL. Entity linking systems can be complex and we present a framework for analysing their different components, which we use to analyse three seminal systems which are evaluated on a common dataset and we show the importance of precise search for linking. The Text Analysis Conference (TAC) is a major venue for NEL research. We report on our submissions to the entity linking shared task in 2010, 2011 and 2012. The information required to disambiguate entities is often found in the text, close to the mention. We explore apposition, a common way for authors to provide information about entities. We model syntactic and semantic restrictions with a joint model that achieves state-of-the-art apposition extraction performance. We generalise from apposition to examine local descriptions specified close to the mention. We add local description to our state-of-the-art linker by using patterns to extract the descriptions and matching against this restricted context. Not only does this make for a more precise match, we are also able to model failure to match. Local descriptions help disambiguate entities, further improving our state-of-the-art linker. The work in this thesis seeks to link textual entity mentions to knowledge bases. Linking is important for any task where external world knowledge is used and resolving ambiguity is fundamental to advancing research into these problems

Effective distant supervision for end-to-end knowledge base population systems

The growing amounts of textual data require automatic methods for structuring relevant information so that it can be further processed by computers and systematically accessed by humans. The scenario dealt with in this dissertation is known as Knowledge Base Population (KBP), where relational information about entities is retrieved from a large text collection and stored in a database, structured according to a pre-specified schema. Most of the research in this dissertation is placed in the context of the KBP benchmark of the Text Analysis Conference (TAC KBP), which provides a test-bed to examine all steps in a complex end-to-end relation extraction setting. In this dissertation a new state of the art for the TAC KBP benchmark was achieved by focussing on the following research problems: (1) The KBP task was broken down into a modular pipeline of sub-problems, and the most pressing issues were identified and quantified at all steps. (2) The quality of semi-automatically generated training data was increased by developing noise-reduction methods, decreasing the influence of false-positive training examples. (3) A focus was laid on fine-grained entity type modelling, entity expansion, entity matching and tagging, to maintain as much recall as possible on the relational argument level. (4) A new set of effective methods for generating training data, encoding features and training relational classifiers was developed and compared with previous state-of-the-art methods.Die wachsende Menge an Textdaten erfordert Methoden, relevante Informationen so zu strukturieren, dass sie von Computern weiterverarbeitet werden können, und dass Menschen systematisch auf sie zugreifen können. Das in dieser Dissertation behandelte Szenario ist unter dem Begriff Knowledge Base Population (KBP) bekannt. Hier werden relationale Informationen über Entitäten aus großen Textbeständen automatisch zusammengetragen und gemäß einem vorgegebenen Schema strukturiert. Ein Großteil der Forschung der vorliegenden Dissertation ist im Kontext des TAC KBP Vergleichstests angesiedelt. Dieser stellt ein Testumfeld dar, um alle Schritte eines anfragebasierten Relationsextraktions-Systems zu untersuchen. Die in der vorliegenden Dissertation entwickelten Verfahren setzen einen neuen Standard für TAC KBP. Dies wurde durch eine Schwerpunktsetzung auf die folgenden Forschungsfragen erreicht: Erstens wurden die wichtigsten Unterprobleme von KBP identifiziert und die jeweiligen Effekte genau quantifiziert. Zweitens wurde die Qualität von halbautomatischen Trainingsdaten durch Methoden erhöht, die den Einfluss von falsch positiven Trainingsbeispielen verringern. Drittens wurde ein Schwerpunkt auf feingliedrige Typmodellierung, die Expansion von Entitätennamen und das Auffinden von Entitäten gelegt, um eine größtmögliche Abdeckung von relationalen Argumenten zu erreichen. Viertens wurde eine Reihe von neuen leistungsstarken Methoden entwickelt und untersucht, um Trainingsdaten zu erzeugen, Klassifizierungsmerkmale zu kodieren und relationale Klassifikatoren zu trainieren

Effective distant supervision for end-to-end knowledge base population systems

The growing amounts of textual data require automatic methods for structuring relevant information so that it can be further processed by computers and systematically accessed by humans. The scenario dealt with in this dissertation is known as Knowledge Base Population (KBP), where relational information about entities is retrieved from a large text collection and stored in a database, structured according to a pre-specified schema. Most of the research in this dissertation is placed in the context of the KBP benchmark of the Text Analysis Conference (TAC KBP), which provides a test-bed to examine all steps in a complex end-to-end relation extraction setting. In this dissertation a new state of the art for the TAC KBP benchmark was achieved by focussing on the following research problems: (1) The KBP task was broken down into a modular pipeline of sub-problems, and the most pressing issues were identified and quantified at all steps. (2) The quality of semi-automatically generated training data was increased by developing noise-reduction methods, decreasing the influence of false-positive training examples. (3) A focus was laid on fine-grained entity type modelling, entity expansion, entity matching and tagging, to maintain as much recall as possible on the relational argument level. (4) A new set of effective methods for generating training data, encoding features and training relational classifiers was developed and compared with previous state-of-the-art methods.Die wachsende Menge an Textdaten erfordert Methoden, relevante Informationen so zu strukturieren, dass sie von Computern weiterverarbeitet werden können, und dass Menschen systematisch auf sie zugreifen können. Das in dieser Dissertation behandelte Szenario ist unter dem Begriff Knowledge Base Population (KBP) bekannt. Hier werden relationale Informationen über Entitäten aus großen Textbeständen automatisch zusammengetragen und gemäß einem vorgegebenen Schema strukturiert. Ein Großteil der Forschung der vorliegenden Dissertation ist im Kontext des TAC KBP Vergleichstests angesiedelt. Dieser stellt ein Testumfeld dar, um alle Schritte eines anfragebasierten Relationsextraktions-Systems zu untersuchen. Die in der vorliegenden Dissertation entwickelten Verfahren setzen einen neuen Standard für TAC KBP. Dies wurde durch eine Schwerpunktsetzung auf die folgenden Forschungsfragen erreicht: Erstens wurden die wichtigsten Unterprobleme von KBP identifiziert und die jeweiligen Effekte genau quantifiziert. Zweitens wurde die Qualität von halbautomatischen Trainingsdaten durch Methoden erhöht, die den Einfluss von falsch positiven Trainingsbeispielen verringern. Drittens wurde ein Schwerpunkt auf feingliedrige Typmodellierung, die Expansion von Entitätennamen und das Auffinden von Entitäten gelegt, um eine größtmögliche Abdeckung von relationalen Argumenten zu erreichen. Viertens wurde eine Reihe von neuen leistungsstarken Methoden entwickelt und untersucht, um Trainingsdaten zu erzeugen, Klassifizierungsmerkmale zu kodieren und relationale Klassifikatoren zu trainieren

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

DBpedia Spotlight: Shedding Light on the Web of Documents

Interlinking text documents with Linked Open Data enables the Web of Data to be used as background knowledge within document-oriented applications such as search and faceted browsing. As a step towards interconnecting the Web of Documents with the Web of Data, we developed DBpedia Spotlight, a system for automatically annotating text documents with DBpedia URIs. DBpedia Spotlight allows users to congure the annotations to their specic needs through the DBpedia Ontology and quality measures such as prominence, topical pertinence, contextual ambiguity and disambiguation condence. We compare our approach with the state of the art in disambiguation, and evaluate our results in light of three baselines and six publicly available annotation systems, demonstrating the competitiveness of our system. DBpedia Spotlight is shared as open source and deployed as a Web Service freely available for public use

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation