Web Relation Extraction with Distant Supervision

Being able to find relevant information about prominent entities quickly is the main reason to use a search engine. However, with large quantities of information on the World Wide Web, real time search over billions of Web pages can waste resources and the end user’s time. One of the solutions to this is to store the answer to frequently asked general knowledge queries, such as the albums released by a musical artist, in a more accessible format, a knowledge base. Knowledge bases can be created and maintained automatically by using information extraction methods, particularly methods to extract relations between proper names (named entities). A group of approaches for this that has become popular in recent years are distantly supervised approaches as they allow to train relation extractors without text-bound annotation, using instead known relations from a knowledge base to heuristically align them with a large textual corpus from an appropriate domain. This thesis focuses on researching distant supervision for the Web domain. A new setting for creating training and testing data for distant supervision from the Web with entity-specific search queries is introduced and the resulting corpus is published. Methods to recognise noisy training examples as well as methods to combine extractions based on statistics derived from the background knowledge base are researched. Using co-reference resolution methods to extract relations from sentences which do not contain a direct mention of the subject of the relation is also investigated. One bottleneck for distant supervision for Web data is identified to be named entity recognition and classification (NERC), since relation extraction methods rely on it for identifying relation arguments. Typically, existing pre-trained tools are used, which fail in diverse genres with non-standard language, such as the Web genre. The thesis explores what can cause NERC methods to fail in diverse genres and quantifies different reasons for NERC failure. Finally, a novel method for NERC for relation extraction is proposed based on the idea of jointly training the named entity classifier and the relation extractor with imitation learning to reduce the reliance on external NERC tools. This thesis improves the state of the art in distant supervision for knowledge base population, and sheds light on and proposes solutions for issues arising for information extraction for not traditionally studied domains

Mining entity and relation structures from text: An effort-light approach

In today's computerized and information-based society, text data is rich but often also "messy". We are inundated with vast amounts of text data, written in different genres (from grammatical news articles and scientific papers to noisy social media posts), covering topics in various domains (e.g., medical records, corporate reports, and legal acts). Can computational systems automatically identify various real-world entities mentioned in a new corpus and use them to summarize recent news events reliably? Can computational systems capture and represent different relations between biomedical entities from massive and rapidly emerging life science literature? How might computational systems represent the factual information contained in a collection of medical reports to support answering detailed queries or running data mining tasks? While people can easily access the documents in a gigantic collection with the help of data management systems, they struggle to gain insights from such a large volume of text data: document understanding calls for in-depth content analysis, content analysis itself may require domain-specific knowledge, and over a large corpus, a complete read and analysis by domain experts will invariably be subjective, time-consuming and relatively costly. To turn such massive, unstructured text corpora into machine-readable knowledge, one of the grand challenges is to gain an understanding of the typed entity and relation structures in the corpus. This thesis focuses on developing principled and scalable methods for extracting typed entities and relationship with light human annotation efforts, to overcome the barriers in dealing with text corpora of various domains, genres and languages. In addition to our effort-light methodologies, we also contribute effective, noise-robust models and real-world applications in two main problems: - Identifying Typed Entities: We show how to perform data-driven text segmentation to recognize entities mentioned in text as well as their surrounding relational phrases, and infer types for entity mentions by propagating "distant supervision" (from external knowledge bases) via relational phrases. In order to resolve data sparsity issue during propagation, we complement the type propagation with clustering of functionally similar relational phrases based on their redundant occurrences in large corpus. Apart from entity recognition and coarse-grained typing, we claim that fine-grained entity typing is beneficial for many downstream applications and very challenging due to the context-agnostic label assignment in distant supervision, and we present principled, efficient models and algorithms for inferring fine-grained type path for entity mention based on the sentence context. - Extracting Typed Entity Relationships: We extend the idea of entity recognition and typing to extract relationships between entity mentions and infer their relation types. We show how to effectively model the noisy distant supervision for relationship extraction, and how to avoid the error propagation usually happened in incremental extraction pipeline by integrating typing of entities and relationships in a principled framework. The proposed approach leverages noisy distant supervision for both entities and relationships, and simultaneously learn to uncover the most confident labels as well as modeling the semantic similarity between true labels and text features. In practice, text data is often highly variable: corpora from different domains, genres or languages have typically required for effective processing a wide range of language resources (e.g., grammars, vocabularies, and gazetteers). The “massive” and “messy” nature of text data poses significant challenges to creating tools for automated extraction of entity and relation structures that scale with text volume. State-of-the-art information extraction systems have relied on large amounts of task-specific labeled data (e.g., annotating terrorist attack-related entities in web forum posts written in Arabic), to construct machine-learning models (e.g., deep neural networks). However, even though domain experts can manually create high-quality training data for specific tasks as needed, both the scale and efficiency of such a manual process are limited. This thesis harnesses the power of ``big text data'' and focuses on creating generic solutions for efficient construction of customized machine-learning models for mining typed entities and relationships, relying on only limited amounts of (or even no) task-specific training data. The approaches developed in the thesis are thus general and applicable to all kinds of text corpora in different natural languages, enabling quick deployment of data mining applications. We provide scalable algorithmic approaches that leverage external knowledge bases as sources of supervision and exploit data redundancy in massive text corpora, and we show how to use them in large-scale, real-world applications, including structured exploration and analysis of life sciences literature, extracting document facets from technical documents, document summarization, entity attribute discovery, and open-domain information extraction

Towards the extraction of cross-sentence relations through event extraction and entity coreference

Cross-sentence relation extraction deals with the extraction of relations beyond the sentence boundary. This thesis focuses on two of the NLP tasks which are of importance to the successful extraction of cross-sentence relation mentions: event extraction and coreference resolution. The first part of the thesis focuses on addressing data sparsity issues in event extraction. We propose a self-training approach for obtaining additional labeled examples for the task. The process starts off with a Bi-LSTM event tagger trained on a small labeled data set which is used to discover new event instances in a large collection of unstructured text. The high confidence model predictions are selected to construct a data set of automatically-labeled training examples. We present several ways in which the resulting data set can be used for re-training the event tagger in conjunction with the initial labeled data. The best configuration achieves statistically significant improvement over the baseline on the ACE 2005 test set (macro-F1), as well as in a 10-fold cross validation (micro- and macro-F1) evaluation. Our error analysis reveals that the augmentation approach is especially beneficial for the classification of the most under-represented event types in the original data set. The second part of the thesis focuses on the problem of coreference resolution. While a certain level of precision can be reached by modeling surface information about entity mentions, their successful resolution often depends on semantic or world knowledge. This thesis investigates an unsupervised source of such knowledge, namely distributed word representations. We present several ways in which word embeddings can be utilized to extract features for a supervised coreference resolver. Our evaluation results and error analysis show that each of these features helps improve over the baseline coreference system’s performance, with a statistically significant improvement (CoNLL F1) achieved when the proposed features are used jointly. Moreover, all features lead to a reduction in the amount of precision errors in resolving references between common nouns, demonstrating that they successfully incorporate semantic information into the process

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-frequent, coarse-grained, and time-invariant concepts from formal texts. In real applications, however, it is necessary to extract less-frequent, fine-grained, and time-varying conceptual knowledge and build taxonomy in an evolving manner. In this paper, we introduce an approach to implementing and deploying the conceptual graph at Alibaba. Specifically, We propose a framework called AliCG which is capable of a) extracting fine-grained concepts by a novel bootstrapping with alignment consensus approach, b) mining long-tail concepts with a novel low-resource phrase mining approach, c) updating the graph dynamically via a concept distribution estimation method based on implicit and explicit user behaviors. We have deployed the framework at Alibaba UC Browser. Extensive offline evaluation as well as online A/B testing demonstrate the efficacy of our approach.Comment: Accepted by KDD 2021 (Applied Data Science Track

Extracting phenotype-gene relations from biomedical literature using distant supervision and deep learning

Tese de mestrado em Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019As relações entre fenótipos humanos e genes são fundamentais para entender completamente a origem de algumas abnormalidades fenotípicas e as suas doenças associadas. A literatura biomédica é a fonte mais abrangente dessas relações. Diversas ferramentas de extração de relações têm sido propostas para identificar relações entre conceitos em texto muito heterogéneo ou não estruturado, utilizando algoritmos de supervisão distante e aprendizagem profunda. Porém, a maioria dessas ferramentas requer um corpus anotado e não há nenhum corpus disponível anotado com relações entre fenótipos humanos e genes. Este trabalho apresenta o corpus Phenotype-Gene Relations (PGR), um corpus padrão-prata de anotações de fenótipos humanos e genes e as suas relações (gerado de forma automática) e dois módulos de extração de relações usando um algoritmo de distantly supervised multi-instance learning e um algoritmo de aprendizagem profunda com ontologias biomédicas. O corpus PGR consiste em 1712 resumos de artigos, 5676 anotações de fenótipos humanos, 13835 anotações de genes e 4283 relações. Os resultados do corpus foram parcialmente avaliados por oito curadores, todos investigadores nas áreas de Biologia e Bioquímica, obtendo uma precisão de 87,01%, com um valor de concordância inter-curadores de 87,58%. As abordagens de supervisão distante (ou supervisão fraca) combinam um corpus não anotado com uma base de dados para identificar e extrair entidades do texto, reduzindo a quantidade de esforço necessário para realizar anotações manuais. A distantly supervised multi-instance learning aproveita a supervisão distante e um sparse multi-instance learning algorithm para treinar um classificador de extracção de relações, usando uma base de dados padrão-ouro de relações entre fenótipos humanos e genes. As ferramentas de aprendizagem profunda de extração de relações, para tarefas de prospeção de textos biomédicos, raramente tiram proveito dos recursos específicos existentes para cada domínio, como as ontologias biomédicas. As ontologias biomédicas desempenham um papel fundamental, fornecendo informações semânticas e de ancestralidade sobre uma entidade. Este trabalho utilizou a Human Phenotype Ontology e a Gene Ontology, para representar cada par candidato como a sequência de relações entre os seus ancestrais para cada ontologia. O corpus de teste PGR foi aplicado aos módulos de extração de relações desenvolvidos, obtendo resultados promissores, nomeadamente 55,00% (módulo de aprendizagem profunda) e 73,48% (módulo de distantly supervised multi-instance learning) na medida-F. Este corpus de teste também foi aplicado ao BioBERT, um modelo de representação de linguagem biomédica pré-treinada para prospeção de texto biomédico, obtendo 67,16% em medida-F.Human phenotype-gene relations are fundamental to fully understand the origin of some phenotypic abnormalities and their associated diseases. Biomedical literature is the most comprehensive source of these relations. Several relation extraction tools have been proposed to identify relations between concepts in highly heterogeneous or unstructured text, namely using distant supervision and deep learning algorithms. However, most of these tools require an annotated corpus, and there is no corpus available annotated with human phenotype-gene relations. This work presents the Phenotype-Gene Relations (PGR) corpus, a silver standard corpus of human phenotype and gene annotations and their relations (generated in a fully automated manner), and two relation extraction modules using a distantly supervised multi-instance learning algorithm, and an ontology based deep learning algorithm. The PGR corpus consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations. The corpus results were partially evaluated by eight curators, all working in the fields of Biology and Biochemistry, obtaining a precision of 87.01%, with an inter-curator agreement score of 87.58%. Distant supervision (or weak supervision) approaches combine an unlabeled corpus with a knowledge base to identify and extract entities from text, reducing the amount of manual effort necessary. Distantly supervised multi-instance learning takes advantage of distant supervision and a sparse multi-instance learning algorithm to train a relation extraction classifier, using a gold standard knowledge base of human phenotype-gene relations. Deep learning relation extraction tools, for biomedical text mining tasks, rarely take advantage of existing domain-specific resources, such as biomedical ontologies. Biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. This work used the Human Phenotype Ontology and the Gene Ontology, to represent each candidate pair as the sequence of relations between its ancestors for each ontology. The PGR test-set was applied to the developed relation extraction modules, obtaining promising results, namely 55.00% (deep learning module), and 73.48% (distantly supervised multi-instance learning module) in F-measure. This test-set was also applied to BioBERT, a pre-trained biomedical language representation model for biomedical text mining, obtaining 67.16% in F-measure