Scalable topical phrase mining from text corpora

El-Kishky, Ahmed; Song, Yanglei; Wang, Chi; Voss, Clare R.; Han, Jiawei

doi:10.14778/2735508.2735519

Cited by 165 publications

(139 citation statements)

References 14 publications

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“…Experimental results show that this algorithm generates topics which are more interpretable than traditional LDA model. [9] -This algorithm first extracts phrases using a method similar to frequent pattern mining and then train a modified LDA model on the "bag-of phrases" input. This algorithm is found to be performing better than topical n-gram model (TNG) and a number of phrase discovering topic models such as phrase discovering topic model (PDLDA)…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

“…A new model that performs phrase segmentation along with topic modeling was introduced [8] but failed to work with datasets having large number of files. TopMine [9], a system capable of mining topical phrase mining, is introduced recently, which implements a two step process for discovering phrases from text and for training a tradition topic model such as LDA. The assumption associated with this system, which says words in the same phrase must be assigned with the same topic, is not happening most of the time in practical scenario.…”

Section: State Of the Art In Concept Extractionmentioning

confidence: 99%

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A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

Anoop¹,

Asharaf²

2017

IJIMAI

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The task of mining large unstructured text archives, extracting useful patterns and then organizing them into a knowledgebase has attained a great attention due to its vast array of immediate applications in business. Businesses thus demand new and efficient algorithms for leveraging potentially useful patterns from heterogeneous data sources that produce huge volumes of unstructured data. Due to the ability to bring out hidden themes from large text repositories, topic modeling algorithms attained significant attention in the recent past. This paper proposes an efficient and scalable method which is guided by topic modeling for extracting concepts and relationships from e-commerce product descriptions and organizing them into knowledgebase. Semantic graphs can be generated from such a knowledgebase on which meaning aware product discovery experience can be built for potential buyers. Extensive experiments using proposed unsupervised algorithms with e-commerce product descriptions collected from open web shows that our proposed method outperforms some of the existing methods of leveraging concepts and relationships so that efficient knowledgebase construction is possible.

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Section: Evaluation Of Resultsmentioning

confidence: 99%

Section: State Of the Art In Concept Extractionmentioning

confidence: 99%

A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

Anoop¹,

Asharaf²

2017

IJIMAI

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“…Statistical pattern mining [El-Kishky et al, 2015;Danilevsky et al, 2014; Supervised chunking trained from Penn Treebank Topic hierarchy / Taxonomy construction Combine statistical pattern mining with information networks [Wang et al, 2014] Lexical/Syntactic patterns (e.g., COLING2014 workshop on taxonomy construction) Entity Linking Graph alignment [Li et al, 2013] TAC-KBP Entity Linking methods and Wikification Relation discovery Hierarchical clustering [Wang et al, 2012] ACE relation extraction, bootstrapping Sentiment Analysis…”

Section: Nlp Methods Phrase Mining / Chunkingmentioning

confidence: 99%

Successful Data Mining Methods for NLP

Sun

2015

Tutorials

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Historically Natural Language Processing (NLP) focuses on unstructured data (speech and text) understanding while Data Mining (DM) mainly focuses on massive, structured or semi-structured datasets. The general research directions of these two fields also have followed different philosophies and principles. For example, NLP aims at deep understanding of individual words, phrases and sentences ("micro-level"), whereas DM aims to conduct a high-level understanding, discovery and synthesis of the most salient information from a large set of documents when working on text data ("macro-level"). But they share the same goal of distilling knowledge from data. In the past five years, these two areas have had intensive interactions and thus mutually enhanced each other through many successful text mining tasks. This positive progress mainly benefits from some innovative intermediate representations such as "heterogeneous information networks" [Han et al., 2010, Sun et al., 2012b.However, successful collaborations between any two fields require substantial mutual understanding, patience and passion among researchers. Similar to the applications of machine learning techniques in NLP, there is usually a gap of at least several years between the creation of a new DM approach and its first successful application in NLP. More importantly, many DM approaches such as gSpan [Yan and Han, 2002] and RankClus [Sun et al., 2009a] have demonstrated their power on structured data. But they remain relatively unknown in the NLP community, even though there are many obvious potential applications. On the other hand, compared to DM, the NLP community has paid more attention to developing large-scale data annotations, resources, shared tasks which cover a wide range of multiple genres and multiple domains. NLP can also provide the basic building blocks for many DM tasks such as text cube construction [Tao et al., 2014]. Therefore in many scenarios, for the same approach the NLP experiment setting is often much closer to real-world applications than its DM counterpart. We would like to share the experiences and lessons from our extensive inter-disciplinary collaborations in the past five years. The primary goal of this tutorial is to bridge the knowledge gap between these two fields and speed up the transition process. We will introduce two types of DM methods: (1). those state-of-the-art DM methods that have already been proven effective for NLP; and (2). some newly developed DM methods that we believe will fit into some specific NLP problems. In addition, we aim to suggest some new research directions in order to better marry these two areas and lead to more fruitful outcomes. The tutorial will thus be useful for researchers from both communities. We will try to provide a concise roadmap of recent perspectives and results, as well as point to the related DM software and resources, and NLP data sets that are available to both research communities. OutlineWe will focus on the following three perspectives. Where do NLP and DM MeetWe will first...

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“…We further apply n-gram testing techniques [30,10,11] to select from N the n-grams that have best word collocation for each n ≥ 2, in order to obtain more salient n-grams as candidates. Compared to NLP techniques such as chunking and dependency parsing, n-gram testing methods do not rely on any model training and are domain-independent.…”

Section: Candidate Generationmentioning

confidence: 99%

Synonym Discovery for Structured Entities on Heterogeneous Graphs

Ren

Cheng

2015

Proceedings of the 24th International Conference on World Wide Web

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With the increasing use of entities in serving people's daily information needs, recognizing synonyms-different ways people refer to the same entity-has become a crucial task for many entity-leveraging applications. Previous works often take a "literal" view of the entity, i.e., its string name. In this work, we propose adopting a "structured" view of each entity by considering not only its string name, but also other important structured attributes. Unlike existing query logbased methods, we delve deeper to explore sub-queries, and exploit tailed synonyms and tailed web pages for harvesting more synonyms. A general, heterogeneous graph-based data model which encodes our problem insights is designed by capturing three key concepts (synonym candidate, web page and keyword) and different types of interactions between them. We cast the synonym discovery problem into a graph-based ranking problem and demonstrate the existence of a closed-form optimal solution for outputting entity synonym scores. Experiments on several real-life domains demonstrate the effectiveness of our proposed method.

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Scalable topical phrase mining from text corpora

Cited by 165 publications

References 14 publications

A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

Successful Data Mining Methods for NLP

Synonym Discovery for Structured Entities on Heterogeneous Graphs

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