Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e.,~linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods

Analysing entity context in multilingual Wikipedia to support entity-centric retrieval applications

Representation of influential entities, such as famous people and multinational corporations, on the Web can vary across languages, reflecting language-specific entity aspects as well as divergent views on these entities in different communities. A systematic analysis of language specific entity contexts can provide a better overview of the existing aspects and support entity-centric retrieval applications over multilingual Web data. An important source of cross-lingual information about influential entities is Wikipedia — an online community-created encyclopaedia — containing more than 280 language editions. In this paper we focus on the extraction and analysis of the language-specific entity contexts from different Wikipedia language editions over multilingual data. We discuss alternative ways such contexts can be built, including graph-based and article-based contexts. Furthermore, we analyse the similarities and the differences in these contexts in a case study including 80 entities and five Wikipedia language editions

Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding

Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora. To account for potential hierarchical topic structures, hierarchical topic models generalize flat topic models by incorporating latent topic hierarchies into their generative modeling process. However, due to their purely unsupervised nature, the learned topic hierarchy often deviates from users' particular needs or interests. To guide the hierarchical topic discovery process with minimal user supervision, we propose a new task, Hierarchical Topic Mining, which takes a category tree described by category names only, and aims to mine a set of representative terms for each category from a text corpus to help a user comprehend his/her interested topics. We develop a novel joint tree and text embedding method along with a principled optimization procedure that allows simultaneous modeling of the category tree structure and the corpus generative process in the spherical space for effective category-representative term discovery. Our comprehensive experiments show that our model, named JoSH, mines a high-quality set of hierarchical topics with high efficiency and benefits weakly-supervised hierarchical text classification tasks.Comment: KDD 2020 Research Track. (Code: https://github.com/yumeng5/JoSH

Incorporating Social Context and Domain Knowledge for Entity Recognition

Recognizing entity instances in documents according to a knowl-edge base is a fundamental problem in many data mining applica-tions. The problem is extremely challenging for short documents in complex domains such as social media and biomedical domains. Large concept spaces and instance ambiguity are key issues that need to be addressed. Most of the documents are created in a social context by common authors via social interactions, such as reply and citations. Such social contexts are largely ignored in the instance-recognition liter-ature. How can users ’ interactions help entity instance recognition? How can the social context be modeled so as to resolve the ambi-guity of different instances? In this paper, we propose the SOCINST model to formalize the problem into a probabilistic model. Given a set of short document

Cross collection aspect based opinion mining using topic models

Aspect based opinion mining is the automated science of identifying and extracting sentiments associated to individual aspects in a text document. Over the years this science has emerged to be a cornerstone for analysis of public opinion on consumer products and social-political events. The task is more fruitful and likewise more challenging when comparison of opinion on aspects of multiple entities is of essence. Different methods in literature have attempted to extract aspects in a single collection or collection by collection across multiple collection. These approaches do not appeal when number of collections is large and hence su er significant performance drawbacks. In this work we perform aspect based opinion mining across contrasting multiple collections, simultaneously. We utilize existing cross collection topic models to identify topics that prevail across multiple collections, we propose a topic refinement algorithm that successfully converts these topics into semantically coherent and visually identifiable aspects. We compare the quality of aspects extracted by our algorithm to topics returned by two cross collection topic models. Finally we evaluate the accuracy of sentiment scores when measured over features extracted by the two cross collection topic models. We conclude that with proposed improvements cross collection topic models outperform state of art approaches in aspect based sentiment analysis

Search beyond traditional probabilistic information retrieval

"This thesis focuses on search beyond probabilistic information retrieval. Three ap- proached are proposed beyond the traditional probabilistic modelling. First, term associ- ation is deeply examined. Term association considers the term dependency using a factor analysis based model, instead of treating each term independently. Latent factors, con- sidered the same as the hidden variables of ""eliteness"" introduced by Robertson et al. to gain understanding of the relation among term occurrences and relevance, are measured by the dependencies and occurrences of term sequences and subsequences. Second, an entity-based ranking approach is proposed in an entity system named ""EntityCube"" which has been released by Microsoft for public use. A summarization page is given to summarize the entity information over multiple documents such that the truly relevant entities can be highly possibly searched from multiple documents through integrating the local relevance contributed by proximity and the global enhancer by topic model. Third, multi-source fusion sets up a meta-search engine to combine the ""knowledge"" from different sources. Meta-features, distilled as high-level categories, are deployed to diversify the baselines. Three modified fusion methods are employed, which are re- ciprocal, CombMNZ and CombSUM with three expanded versions. Through extensive experiments on the standard large-scale TREC Genomics data sets, the TREC HARD data sets and the Microsoft EntityCube Web collections, the proposed extended models beyond probabilistic information retrieval show their effectiveness and superiority.

Towards Name Disambiguation: Relational, Streaming, and Privacy-Preserving Text Data

In the real world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesakes of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensics. To resolve this issue, the name disambiguation task 1 is designed to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing algorithms for this task mainly suffer from the following drawbacks. First, the majority of existing solutions substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable in privacy sensitive domains. Instead we solve the name disambiguation task in restricted setting by leveraging only the relational data in the form of anonymized graphs. Second, most of the existing works for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task should be performed in an online streaming fashion in order to identify records of new ambiguous entities having no preexisting records. Finally, we investigate the potential disclosure risk of textual features used in name disambiguation and propose several algorithms to tackle the task in a privacy-aware scenario. In summary, in this dissertation, we present a number of novel approaches to address name disambiguation tasks from the above three aspects independently, namely relational, streaming, and privacy preserving textual data

Deliverable D2.3 Specification of Web mining process for hypervideo concept identification

This deliverable presents a state-of-art and requirements analysis report for the web mining process as part of the WP2 of the LinkedTV project. The deliverable is divided into two subject areas: a) Named Entity Recognition (NER) and b) retrieval of additional content. The introduction gives an outline of the workflow of the work package, with a subsection devoted to relations with other work packages. The state-of-art review is focused on prospective techniques for LinkedTV. In the NER domain, the main focus is on knowledge-based approaches, which facilitate disambiguation of identified entities using linked open data. As part of the NER requirement analysis, the first tools developed are described and evaluated (NERD, SemiTags and THD). The area of linked additional content is broader and requires a more thorough analysis. A balanced overview of techniques for dealing with the various knowledge sources (semantic web resources, web APIs and completely unstructured resources from a white list of web sites) is presented. The requirements analysis comes out of the RBB and Sound and Vision LinkedTV scenarios