Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions 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 be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.Comment: to appear in CIKM 201

Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives

Name ambiguity is common in academic digital libraries, such as multiple authors having the same name. This creates challenges for academic data management and analysis, thus name disambiguation becomes necessary. The procedure of name disambiguation is to divide publications with the same name into different groups, each group belonging to a unique author. A large amount of attribute information in publications makes traditional methods fall into the quagmire of feature selection. These methods always select attributes artificially and equally, which usually causes a negative impact on accuracy. The proposed method is mainly based on representation learning for heterogeneous networks and clustering and exploits the self-attention technology to solve the problem. The presentation of publications is a synthesis of structural and semantic representations. The structural representation is obtained by meta-path-based sampling and a skip-gram-based embedding method, and meta-path level attention is introduced to automatically learn the weight of each feature. The semantic representation is generated using NLP tools. Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method. The experimental results show the superiority of our new method for capturing the most attributes from publications and reducing the impact of redundant information

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

LAGOS-AND: A Large Gold Standard Dataset for Scholarly Author Name Disambiguation

In this paper, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS-AND, two large, gold-standard datasets for author name disambiguation (AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our LAGOS-AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5M citations authored by 798K unique authors (LAGOS-AND-BLOCK) and close to 1M instances (LAGOS-AND-PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available.Comment: 33 pages, 7 tables, 7 figure

The role of knowledge in determining identity of long-tail entities

The NIL entities do not have an accessible representation, which means that their identity cannot be established through traditional disambiguation. Consequently, they have received little attention in entity linking systems and tasks so far. Given the non-redundancy of knowledge on NIL entities, the lack of frequency priors, their potentially extreme ambiguity, and numerousness, they form an extreme class of long-tail entities and pose a great challenge for state-of-the-art systems. In this paper, we investigate the role of knowledge when establishing the identity of NIL entities mentioned in text. What kind of knowledge can be applied to establish the identity of NILs? Can we potentially link to them at a later point? How to capture implicit knowledge and fill knowledge gaps in communication? We formulate and test hypotheses to provide insights to these questions. Due to the unavailability of instance-level knowledge, we propose to enrich the locally extracted information with profiling models that rely on background knowledge in Wikidata. We describe and implement two profiling machines based on state-of-the-art neural models. We evaluate their intrinsic behavior and their impact on the task of determining identity of NIL entities

Collective Entity Resolution In Relational Data

Many databases contain imprecise references to real-world entities. For example, a social-network database records names of people. But different people can go by the same name and there may be different observed names referring to the same person. The goal of entity resolution is to determine the mapping from database references to discovered real-world entities. Traditional entity resolution approaches consider approximate matches between attributes of individual references, but this does not always work well. In many domains, such as social networks and academic circles, the underlying entities exhibit strong ties to each other, and as a result, their references often co-occur in the data. In this dissertation, I focus on the use of such co-occurrence relationships for jointly resolving entities. I refer to this problem as `collective entity resolution'. First, I propose a relational clustering algorithm for iteratively discovering entities by clustering references taking into account the clusters of co-occurring references. Next, I propose a probabilistic generative model for collective resolution that finds hidden group structures among the entities and uses the latent groups as evidence for entity resolution. One of my contributions is an efficient unsupervised inference algorithm for this model using Gibbs Sampling techniques that discovers the most likely number of entities. Both of these approaches improve performance over attribute-only baselines in multiple real world and synthetic datasets. I also perform a theoretical analysis of how the structural properties of the data affect collective entity resolution and verify the predicted trends experimentally. In addition, I motivate the problem of query-time entity resolution. I propose an adaptive algorithm that uses collective resolution for answering queries by recursively exploring and resolving related references. This enables resolution at query-time, while preserving the performance benefits of collective resolution. Finally, as an application of entity resolution in the domain of natural language processing, I study the sense disambiguation problem and propose models for collective sense disambiguation using multiple languages that outperform other unsupervised approaches

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Word Sense Consistency in Statistical and Neural Machine Translation

Different senses of source words must often be rendered by different words in the target language when performing machine translation (MT). Selecting the correct translation of polysemous words can be done based on the contexts of use. However, state-of-the-art MT algorithms generally work on a sentence-by-sentence basis that ignores information across sentences. In this thesis, we address this problem by studying novel contextual approaches to reduce source word ambiguity in order to improve translation quality. The thesis consists of two parts: the first part is devoted to methods for correcting ambiguous word translations by enforcing consistency across sentences, and the second part investigates sense-aware MT systems that address the ambiguity problem for each word. In the first part, we propose to reduce word ambiguity by using lexical consistency, starting from the one-sense-per-discourse hypothesis. If a polysemous word appears multiple times in a discourse, it is likely that occurrences will share the same sense. We first improve the translation of polysemous nouns (Y) in the case when a previous occurrence of a noun as the head of a compound noun phrase (XY) is available in a text. Experiments on two language pairs show that the translations of the targeted polysemous nouns are significantly improved. As compound pairs X Y /Y appear quite infrequently in texts, we extend our work by analysing the repetition of nouns which are not compounds. We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently. We design a classifier to predict translation consistency based on syntactic and semantic features. We integrate the classifiersâ output into MT. We experiment on two language pairs and show that our method closes up to 50% of the gap in BLEU scores between the baseline and an oracle classifier. In the second part of the thesis, we design sense-aware MT systems that (automatically) select the correct translations of ambiguous words by performing word sense disambiguation (WSD). We demonstrate that WSD can improve MT by widening the source context considered when modeling the senses of potentially ambiguous words. We first design three adaptive clustering algorithms, respectively based on k-means, Chinese restaurant process and random walk. For phrase-based statistical MT, we integrate the sense knowledge as an additional feature through a factored model and show that the combination improves the translation from English to five other languages. As the sense integration appears promising for SMT, we also transfer this approach to the newer neural MT models, which are now state of the art. However, unlike SMT, for which it is easier to use linguistic features, NMT uses vectors for word generation and traditional feature incorporation does not work here. We design a sense-aware NMT model that jointly learns the sense knowledge using an attention-based sense selection mechanism and concatenates the learned sense vectors with word vectors during encoding . Such a concatenation outperforms several baselines. The improvements are significant over both overall and analysed ambiguous words over the same language pairs we experiment with SMT. Overall, the thesis proves that lexical consistency and WSD are practical and workable solutions that lead to global improvements in translation in ranges of 0.2 to 1.5 BLEU score