Extracting Keyphrases from Chinese News Articles Using TextRank and Query Log Knowledge

PACLIC 23 / City University of Hong Kong / 3-5 December 200

A User-Centered Concept Mining System for Query and Document Understanding at Tencent

Concepts embody the knowledge of the world and facilitate the cognitive processes of human beings. Mining concepts from web documents and constructing the corresponding taxonomy are core research problems in text understanding and support many downstream tasks such as query analysis, knowledge base construction, recommendation, and search. However, we argue that most prior studies extract formal and overly general concepts from Wikipedia or static web pages, which are not representing the user perspective. In this paper, we describe our experience of implementing and deploying ConcepT in Tencent QQ Browser. It discovers user-centered concepts at the right granularity conforming to user interests, by mining a large amount of user queries and interactive search click logs. The extracted concepts have the proper granularity, are consistent with user language styles and are dynamically updated. We further present our techniques to tag documents with user-centered concepts and to construct a topic-concept-instance taxonomy, which has helped to improve search as well as news feeds recommendation in Tencent QQ Browser. We performed extensive offline evaluation to demonstrate that our approach could extract concepts of higher quality compared to several other existing methods. Our system has been deployed in Tencent QQ Browser. Results from online A/B testing involving a large number of real users suggest that the Impression Efficiency of feeds users increased by 6.01% after incorporating the user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201

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

Abstractive Opinion Tagging

In e-commerce, opinion tags refer to a ranked list of tags provided by the e-commerce platform that reflect characteristics of reviews of an item. To assist consumers to quickly grasp a large number of reviews about an item, opinion tags are increasingly being applied by e-commerce platforms. Current mechanisms for generating opinion tags rely on either manual labelling or heuristic methods, which is time-consuming and ineffective. In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews. The abstractive opinion tagging task comes with three main challenges: (1) the noisy nature of reviews; (2) the formal nature of opinion tags vs. the colloquial language usage in reviews; and (3) the need to distinguish between different items with very similar aspects. To address these challenges, we propose an abstractive opinion tagging framework, named AOT-Net, to generate a ranked list of opinion tags given a large number of reviews. First, a sentence-level salience estimation component estimates each review's salience score. Next, a review clustering and ranking component ranks reviews in two steps: first, reviews are grouped into clusters and ranked by cluster size; then, reviews within each cluster are ranked by their distance to the cluster center. Finally, given the ranked reviews, a rank-aware opinion tagging component incorporates an alignment feature and alignment loss to generate a ranked list of opinion tags. To facilitate the study of this task, we create and release a large-scale dataset, called eComTag, crawled from real-world e-commerce websites. Extensive experiments conducted on the eComTag dataset verify the effectiveness of the proposed AOT-Net in terms of various evaluation metrics.Comment: Accepted by WSDM 202

Automatic keyphrase extraction on Amazon reviews

People are facing severe challenges posed by big data. As an important type of the online text, product reviews have evoked much research interest because of their commercial potential. This thesis takes Amazon camera reviews as the research focus and implements an automatic keyphrase extraction system. The system consists of three modules, including the Crawler module, the Extraction module, and the Web module. The Crawler module is responsible for capturing Amazon product reviews. The Web module is responsible for obtaining user input and displaying the final results. The Extraction module is the core processing module of the system, which analyzes product reviews according to the following sequence: (1) Pre-processing of review data, including removal of stop words and segmentation. ( 2) Candidate keyphrase extraction. Through the Spacy part-of speech tagger and Dependency parser, the dependency relationships of each review sentence are obtained, and then the feature and opinion words are extracted based on several predefined dependency rules. (3) Candidate keyphrase clustering. By using a Latent Dirichlet Allocation (LDA) model, the candidate keyphrases are clustered according to their topics . ( 4) Candidate keyphrase ranking. Two different algorithms, LDA-TFIDF and LDA-MT, are applied to rank the keyphrases in different clusters to get the representative keyphrases. The experimental results show that the system performs well in the task of keyphrase extraction

Applied Deep Learning: Case Studies in Computer Vision and Natural Language Processing

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover classification. In addition, a super-resolution deep model was implemented to further enhance remote sensing images. In the third study, a deep learning-based framework was proposed for fact-checking on social media to spot fake scientific news. The framework leveraged deep learning, information retrieval, and natural language processing techniques to retrieve pertinent scholarly papers for given scientific news and evaluate the credibility of the news

Extracting keywords from tweets

Nos últimos anos, uma enorme quantidade de informações foi disponibilizada na Internet. As redes sociais estão entre as que mais contribuem para esse aumento no volume de dados. O Twitter, em particular, abriu o caminho, enquanto plataforma social, para que pessoas e organizações possam interagir entre si, gerando grandes volumes de dados a partir dos quais é possível extrair informação útil. Uma tal quantidade de dados, permitirá por exemplo, revelar-se importante se e quando, vários indivíduos relatarem sintomas de doença ao mesmo tempo e no mesmo lugar. Processar automaticamente um tal volume de informações e obter a partir dele conhecimento útil, torna-se, no entanto, uma tarefa impossível para qualquer ser humano. Os extratores de palavras-chave surgem neste contexto como uma ferramenta valiosa que visa facilitar este trabalho, ao permitir, de uma forma rápida, ter acesso a um conjunto de termos caracterizadores do documento. Neste trabalho, tentamos contribuir para um melhor entendimento deste problema, avaliando a eficácia do YAKE (um algoritmo de extração de palavras-chave não supervisionado) em cima de um conjunto de tweets, um tipo de texto, caracterizado não só pelo seu reduzido tamanho, mas também pela sua natureza não estruturada. Embora os extratores de palavras-chave tenham sido amplamente aplicados a textos genéricos, como a relatórios, artigos, entre outros, a sua aplicabilidade em tweets é escassa e até ao momento não foi disponibilizado formalmente nenhum conjunto de dados. Neste trabalho e por forma a contornar esse problema optámos por desenvolver e tornar disponível uma nova coleção de dados, um importante contributo para que a comunidade científica promova novas soluções neste domínio. O KWTweet foi anotado por 15 anotadores e resultou em 7736 tweets anotados. Com base nesta informação, pudemos posteriormente avaliar a eficácia do YAKE! contra 9 baselines de extração de palavra-chave não supervisionados (TextRank, KP-Miner, SingleRank, PositionRank, TopicPageRank, MultipartiteRank, TopicRank, Rake e TF.IDF). Os resultados obtidos demonstram que o YAKE! tem um desempenho superior quando comparado com os seus competidores, provando-se assim a sua eficácia neste tipo de textos. Por fim, disponibilizamos uma demo que visa demonstrar o funcionamento do YAKE! Nesta plataforma web, os utilizadores têm a possibilidade de fazer uma pesquisa por utilizador ou hashtag e dessa forma obter as palavras chave mais relevantes através de uma nuvem de palavra

Constructing and modeling text-rich information networks: a phrase mining-based approach

A lot of digital ink has been spilled on "big data" over the past few years, which is often characterized by an explosion of information. Most of this surge owes its origin to the unstructured data in the wild like words, images and video as comparing to the structured information stored in fielded form in databases. The proliferation of text-heavy data is particularly overwhelming, reflected in everyone's daily life in forms of web documents, business reviews, news, social posts, etc. In the mean time, textual data and structured entities often come in intertwined, such as authors/posters, document categories and tags, and document-associated geo locations. With this background, a core research challenge presents itself as how to turn massive, (semi-)unstructured data into structured knowledge. One promising paradigm studied in this dissertation is to integrate structured and unstructured data, constructing an organized heterogeneous information network, and developing powerful modeling mechanisms on such organized network. We name it text-rich information network, since it is an integrated representation of both structured and unstructured textual data. To thoroughly develop the construction and modeling paradigm, this dissertation will focus on forming a scalable data-driven framework and propose a new line of techniques relying on the idea of phrase mining to bridge textual documents and structured entities. We will first introduce the phrase mining method named SegPhrase+ to globally discover semantically meaningful phrases from massive textual data, providing a high quality dictionary for text structuralization. Clearly distinct from previous works that mostly focused on raw statistics of string matching, SegPhrase+ looks into the phrase context and effectively rectifies raw statistics to significantly boost the performance. Next, a novel algorithm based on latent keyphrases is developed and adopted to largely eliminate irregularities in massive text via providing an consistent and interpretable document representation. As a critical process in constructing the network, it uses the quality phrases generated in the previous step as candidates. From them a set of keyphrases are extracted to represent a particular document with inferred strength through a statistical model. After this step, documents become more structured and are consistently represented in the form of a bipartite network connecting documents with quality keyphrases. A more heterogeneous text-rich information network can be constructed by incorporating different types of document-associated entities as additional nodes. Lastly, a general and scalable framework, Tensor2vec, are to be added to trational data minining machanism, as the latter cannot readily solve the problem when the organized heterogeneous network has nodes with different types. Tensor2vec is expected to elegantly handle relevance search, entity classification, summarization and recommendation problems, by making use of higher-order link information and projecting multi-typed nodes into a shared low-dimensional vectorial space such that node proximity can be easily computed and accurately predicted

Keywords at Work: Investigating Keyword Extraction in Social Media Applications

This dissertation examines a long-standing problem in Natural Language Processing (NLP) -- keyword extraction -- from a new angle. We investigate how keyword extraction can be formulated on social media data, such as emails, product reviews, student discussions, and student statements of purpose. We design novel graph-based features for supervised and unsupervised keyword extraction from emails, and use the resulting system with success to uncover patterns in a new dataset -- student statements of purpose. Furthermore, the system is used with new features on the problem of usage expression extraction from product reviews, where we obtain interesting insights. The system while used on student discussions, uncover new and exciting patterns. While each of the above problems is conceptually distinct, they share two key common elements -- keywords and social data. Social data can be messy, hard-to-interpret, and not easily amenable to existing NLP resources. We show that our system is robust enough in the face of such challenges to discover useful and important patterns. We also show that the problem definition of keyword extraction itself can be expanded to accommodate new and challenging research questions and datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145929/1/lahiri_1.pd