Topic model

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In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. The "topics" produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's balance of topics is.

Contents

Topic models are also referred to as probabilistic topic models, which refers to statistical algorithms for discovering the latent semantic structures of an extensive text body. In the age of information, the amount of the written material we encounter each day is simply beyond our processing capacity. Topic models can help to organize and offer insights for us to understand large collections of unstructured text bodies. Originally developed as a text-mining tool, topic models have been used to detect instructive structures in data such as genetic information, images, and networks. They also have applications in other fields such as bioinformatics [1] and computer vision. [2]

History

An early topic model was described by Papadimitriou, Raghavan, Tamaki and Vempala in 1998. [3] Another one, called probabilistic latent semantic analysis (PLSA), was created by Thomas Hofmann in 1999. [4] Latent Dirichlet allocation (LDA), perhaps the most common topic model currently in use, is a generalization of PLSA. Developed by David Blei, Andrew Ng, and Michael I. Jordan in 2002, LDA introduces sparse Dirichlet prior distributions over document-topic and topic-word distributions, encoding the intuition that documents cover a small number of topics and that topics often use a small number of words. [5] Other topic models are generally extensions on LDA, such as Pachinko allocation, which improves on LDA by modeling correlations between topics in addition to the word correlations which constitute topics. Hierarchical latent tree analysis (HLTA) is an alternative to LDA, which models word co-occurrence using a tree of latent variables and the states of the latent variables, which correspond to soft clusters of documents, are interpreted as topics.

Animation of the topic detection process in a document-word matrix. Every column corresponds to a document, every row to a word. A cell stores the frequency of a word in a document, dark cells indicate high word frequencies. Topic models group both documents, which use similar words, as well as words which occur in a similar set of documents. The resulting patterns are called "topics". [6]

Topic models for context information

Approaches for temporal information include Block and Newman's determination of the temporal dynamics of topics in the Pennsylvania Gazette during 1728–1800. Griffiths & Steyvers used topic modeling on abstracts from the journal PNAS to identify topics that rose or fell in popularity from 1991 to 2001 whereas Lamba & Madhusushan [7] used topic modeling on full-text research articles retrieved from DJLIT journal from 1981–2018. In the field of library and information science, Lamba & Madhusudhan [8] [9] [10] [11] applied topic modeling on different Indian resources like journal articles and electronic theses and resources (ETDs). Nelson [12] has been analyzing change in topics over time in the Richmond Times-Dispatch to understand social and political changes and continuities in Richmond during the American Civil War. Yang, Torget and Mihalcea applied topic modeling methods to newspapers from 1829–2008. Mimno used topic modelling with 24 journals on classical philology and archaeology spanning 150 years to look at how topics in the journals change over time and how the journals become more different or similar over time.

Yin et al. [13] introduced a topic model for geographically distributed documents, where document positions are explained by latent regions which are detected during inference.

Chang and Blei [14] included network information between linked documents in the relational topic model, to model the links between websites.

The author-topic model by Rosen-Zvi et al. [15] models the topics associated with authors of documents to improve the topic detection for documents with authorship information.

HLTA was applied to a collection of recent research papers published at major AI and Machine Learning venues. The resulting model is called The AI Tree. The resulting topics are used to index the papers at aipano.cse.ust.hk to help researchers track research trends and identify papers to read, and help conference organizers and journal editors identify reviewers for submissions.

Algorithms

In practice, researchers attempt to fit appropriate model parameters to the data corpus using one of several heuristics for maximum likelihood fit. A recent survey by Blei describes this suite of algorithms. [16] Several groups of researchers starting with Papadimitriou et al. [3] have attempted to design algorithms with probable guarantees. Assuming that the data were actually generated by the model in question, they try to design algorithms that probably find the model that was used to create the data. Techniques used here include singular value decomposition (SVD) and the method of moments. In 2012 an algorithm based upon non-negative matrix factorization (NMF) was introduced that also generalizes to topic models with correlations among topics. [17]

In 2018 a new approach to topic models emerged and was based on Stochastic block model [18]

Topic models for quantitative biomedicine

Topic models are being used also in other contexts. For examples emerged uses of topic models in biology and bioinformatics research. [19] Recently topic models has been used to extract information from dataset of cancers' genomic samples. [20] In this case topics are biological latent variables to be inferred.

See also

Related Research Articles

Information retrieval (IR) is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

Natural language processing Field of computer science and linguistics

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The result is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Text mining, also referred to as text data mining, similar to text analytics, is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005) we can differ three different perspectives of text mining: information extraction, data mining, and a KDD process. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.

Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text. A matrix containing word counts per document is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of rows while preserving the similarity structure among columns. Documents are then compared by taking the cosine of the angle between the two vectors formed by any two columns. Values close to 1 represent very similar documents while values close to 0 represent very dissimilar documents.

Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text, such as newspaper articles, real estate records or paragraphs in a manual. User queries can range from multi-sentence full descriptions of an information need to a few words.

A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in a collection of documents. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix is a specific instance of a document-feature matrix where "features" may refer to other properties of a document besides terms. It is also common to encounter the transpose, or term-document matrix where documents are the columns and terms are the rows. They are useful in the field of natural language processing and computational text analysis. While the value of the cells is commonly the raw count of a given term, there are various schemes for weighting the raw counts such as relative frequency/proportions and tf-idf.

Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature. The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations. For example, "car" is similar to "bus", but is also related to "road" and "driving".

Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing is a statistical technique for the analysis of two-mode and co-occurrence data. In effect, one can derive a low-dimensional representation of the observed variables in terms of their affinity to certain hidden variables, just as in latent semantic analysis, from which PLSA evolved.

Non-negative matrix factorization

Non-negative matrix factorization, also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements. This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms or muscular activity, non-negativity is inherent to the data being considered. Since the problem is not exactly solvable in general, it is commonly approximated numerically.

In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's presence is attributable to one of the document's topics. LDA is an example of a topic model and belongs to the machine learning toolbox and in wider sense to the artificial intelligence toolbox.

Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

In linguistics, statistical semantics applies the methods of statistics to the problem of determining the meaning of words or phrases, ideally through unsupervised learning, to a degree of precision at least sufficient for the purpose of information retrieval.

Distributional semantics is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data. The basic idea of distributional semantics can be summed up in the so-called Distributional hypothesis: linguistic items with similar distributions have similar meanings.

In computer vision, the bag-of-words model sometimes called bag-of-visual-words model can be applied to image classification, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.

In computer vision, the problem of object categorization from image search is the problem of training a classifier to recognize categories of objects, using only the images retrieved automatically with an Internet search engine. Ideally, automatic image collection would allow classifiers to be trained with nothing but the category names as input. This problem is closely related to that of content-based image retrieval (CBIR), where the goal is to return better image search results rather than training a classifier for image recognition.

Collaborative tagging, also known as social tagging or folksonomy, allows users to apply public tags to online items, typically to make those items easier for themselves or others to find later. It has been argued that these tagging systems can provide navigational cues or "way-finders" for other users to explore information. The notion is that given that social tags are labels users create to represent topics extracted from online documents, the interpretation of these tags should allow other users to predict the contents of different documents efficiently. Social tags are arguably more important in exploratory search, in which the users may engage in iterative cycles of goal refinement and exploration of new information, and interpretation of information contents by others will provide useful cues for people to discover topics that are relevant.

In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover the hidden thematic structure of a collection of documents. The algorithm improves upon earlier topic models such as latent Dirichlet allocation (LDA) by modeling correlations between topics in addition to the word correlations which constitute topics. PAM provides more flexibility and greater expressive power than latent Dirichlet allocation. While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as bioinformatics. The model is named for pachinko machines—a game popular in Japan, in which metal balls bounce down around a complex collection of pins until they land in various bins at the bottom.

Dynamic topic models are generative models that can be used to analyze the evolution of (unobserved) topics of a collection of documents over time. This family of models was proposed by David Blei and John Lafferty and is an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents.

Gensim

Gensim is an open-source library for unsupervised topic modeling and natural language processing, using modern statistical machine learning.

Word embedding

In natural language processing (NLP), Word embedding is a term used for the representation of words for text analysis, typically in the form of a real-valued vector that encodes the meaning of the word such that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using a set of language modeling and feature learning techniques where words or phrases from the vocabulary are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per word to a continuous vector space with a much lower dimension.

References

  1. Blei, David (April 2012). "Probabilistic Topic Models". Communications of the ACM. 55 (4): 77–84. doi:10.1145/2133806.2133826. S2CID   753304.
  2. Cao, Liangliang, and Li Fei-Fei. "Spatially coherent latent topic model for concurrent segmentation and classification of objects and scenes." 2007 IEEE 11th International Conference on Computer Vision. IEEE, 2007.
  3. 1 2 Papadimitriou, Christos; Raghavan, Prabhakar; Tamaki, Hisao; Vempala, Santosh (1998). "Latent Semantic Indexing: A probabilistic analysis" (Postscript). Proceedings of ACM PODS: 159–168. doi:10.1145/275487.275505. ISBN   978-0897919968. S2CID   1479546.
  4. Hofmann, Thomas (1999). "Probabilistic Latent Semantic Indexing" (PDF). Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval. Archived from the original (PDF) on 2010-12-14.
  5. Blei, David M.; Ng, Andrew Y.; Jordan, Michael I; Lafferty, John (January 2003). "Latent Dirichlet allocation". Journal of Machine Learning Research . 3: 993–1022. doi:10.1162/jmlr.2003.3.4-5.993.
  6. http://topicmodels.west.uni-koblenz.de/ckling/tmt/svd_ap.html
  7. Lamba, Manika jun (2019). "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study". Scientometrics. 120 (2): 477–505. doi:10.1007/s11192-019-03137-5. ISSN   0138-9130. S2CID   174802673.
  8. Lamba, Manika jun (2019). "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study". Scientometrics. 120 (2): 477–505. doi:10.1007/s11192-019-03137-5. ISSN   0138-9130. S2CID   174802673.
  9. Lamba, Manika jun (2019). "Metadata Tagging and Prediction Modeling: Case Study of DESIDOC Journal of Library and Information Technology (2008-2017)". World Digital Libraries. 12: 33–89. doi:10.18329/09757597/2019/12103 (inactive 2021-01-15). ISSN   0975-7597.CS1 maint: DOI inactive as of January 2021 (link)
  10. Lamba, Manika may (2019). "Author-Topic Modeling of DESIDOC Journal of Library and Information Technology (2008-2017), India". Library Philosophy and Practice.
  11. Lamba, Manika sep (2018). Metadata Tagging of Library and Information Science Theses: Shodhganga (2013-2017) (PDF). ETD2018:Beyond the boundaries of Rims and Oceans. Taiwan,Taipei.
  12. Nelson, Rob. Mining the Dispatch. Digital Scholarship Lab, University of Richmond https://dsl.richmond.edu/dispatch/ . Retrieved 26 March 2021.Missing or empty |title= (help)
  13. Yin, Zhijun (2011). "Geographical topic discovery and comparison". Proceedings of the 20th International Conference on World Wide Web: 247–256. doi:10.1145/1963405.1963443. ISBN   9781450306324. S2CID   17883132.
  14. Chang, Jonathan (2009). "Relational Topic Models for Document Networks" (PDF). Aistats. 9: 81–88.
  15. Rosen-Zvi, Michal (2004). "The author-topic model for authors and documents". Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence: 487–494. arXiv: 1207.4169 .
  16. Blei, David M. (April 2012). "Introduction to Probabilistic Topic Models" (PDF). Comm. ACM. 55 (4): 77–84. doi:10.1145/2133806.2133826. S2CID   753304.
  17. Sanjeev Arora; Rong Ge; Ankur Moitra (April 2012). "Learning Topic Models—Going beyond SVD". arXiv: 1204.1956 [cs.LG].
  18. Martin Gerlach; Tiago Pexioto; Eduardo Altmann (2018). "A network approach to topic models". Science Advances. 4 (7): eaaq1360. arXiv: 1708.01677 . Bibcode:2018SciA....4.1360G. doi:10.1126/sciadv.aaq1360. PMC   6051742 . PMID   30035215.
  19. Liu, L.; Tang, L.; et al. (2016). "An overview of topic modeling and its current applications in bioinformatics". SpringerPlus. 5 (1): 1608. doi:10.1186/s40064-016-3252-8. PMC   5028368 . PMID   27652181. S2CID   16712827.
  20. Valle, F.; Osella, M.; Caselle, M. (2020). "A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data". Cancers. 12 (12): 3799. doi:10.3390/cancers12123799. PMC   7766023 . PMID   33339347. S2CID   229325007.

Further reading