phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | topic modelling of document collections— “In this paper we consider the problem of modeling text corpora and other collections of discrete data.” | |
| claim-type | method— “We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora.” | |
| method — evidence | — “We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation.” | |
| transfer | from probabilistic text modelling: the document-and-words structure of latent Dirichlet allocation carried over to collaborative filtering, with a user standing in for a document and the movies chosen standing in for its words— “A user and the movies chosen are analogous to a document and the words in the document (respectively).” | transfers[0] |
| mechanism | a generative probabilistic model: each document is a finite mixture over latent topics, and each topic a distribution over words— “each item of a collection is modeled as a finite mixture over an underlying set of topics” | mechanisms[0] |
| builds-on | hierarchical Bayesian modeling— “LDA is a three-level hierarchical Bayesian model” | claim_relations[0] |
| contradicts | LSI (latent semantic indexing) methodology— “it is not clear why one should adopt the LSI methodology” | claim_relations[1] |
| extends | Hofmann (1999) probabilistic latent semantic indexing (pLSI) model— “LDA overcomes both of these problems by treating the topic mixture weights as a k-parameter hidden random variable rather than a large set of individual parameters which are explicitly linked to the training set.” | claim_relations[2] |
| contradicts | Hofmann's pLSI treatment of documents— “While Hofmann’s work is a useful step toward probabilistic modeling of text, it is incomplete in that it provides no probabilistic model at the level of documents.” | claim_relations[3] |
| builds-on | de Finetti (1990) representation theorem for exchangeable random variables— “A classic representation theorem due to de Finetti (1990) establishes that any collection of exchangeable random variables has a representation as a mixture distribution—in general an infinite mixture.” | claim_relations[4] |
| builds-on | Nigam et al. (2000) mixture of unigrams model— “If we augment the unigram model with a discrete random topic variable z (Figure 3b), we obtain a mixture of unigrams model (Nigam et al., 2000).” | claim_relations[5] |
| sense | a topic - A LATENT MULTINOMIAL VARIABLE, NAMED TO BORROW A MEANING IT DOES NOT CLAIM: the authors say they call these variables topics in order to exploit text-oriented intuitions, and in the same sentence make no epistemological claims about them beyond representing probability distributions over words; the word appears about a hundred times in the body and the disclaimer once, in a footnote— “We refer to the latent multinomial variables in the LDA model as topics, so as to exploit text-oriented intuitions, but we make no epistemological claims regarding these latent variables beyond their utility in representing probability distributions on sets of words” | senses[0] |
| sense | the language of text collections - A GUIDE TO INTUITION, NOT A DOMAIN: words, documents and corpora are used throughout because they help intuition, while the model itself is stated not to be necessarily tied to text and is applied to collaborative filtering and other collections— “We use the language of text collections throughout the paper, referring to entities such as “words,” “documents,” and “corpora.” This is useful in that it helps to guide intuition, particularly when we introduce latent variables which aim to capture abstract notions such as topics. It is important to note, however, that the LDA model is not… | senses[1] |
| sense | exchangeability - TWO ASSUMPTIONS, ONE OF THEM USUALLY LEFT UNSAID: words within a document are exchangeable, which is the bag-of-words assumption everyone states, and DOCUMENTS are exchangeable too, which the authors say is less often stated formally - the second is what removes time from a corpus— “All of these methods are based on the “bag-of-words” assumption—that the order of words in a document can be neglected. In the language of probability theory, this is an assumption of exchangeability for the words in a document (Aldous, 1985). Moreover, although less often stated formally, these methods also assume that documents are… | senses[2] |
| control | density estimation evaluated by likelihood on a held-out test set, with the corpora treated as unlabelled — held — rules out a model rewarded for memorising the documents it was fitted to— “The documents in the corpora are treated as unlabeled; thus, our goal is density estimation—we wish to achieve high likelihood on a held-out test set.” | controls[0] |
| differs in | Hofmann's pLSI treatment of documents — THE PARAMETER IS THE LEVEL AT WHICH THE MODEL IS PROBABILISTIC. The span: 'While Hofmann's work is a useful step toward probabilistic modeling of text, it is incomplete in that it provides no probabilistic model at the level of documents.' The contradiction is not global - pLSI is granted as a useful step - and it turns on one named thing.— “While Hofmann’s work is a useful step toward probabilistic modeling of text, it is incomplete in that it provides no probabilistic model at the level of documents.”— “in that it provides no probabilistic model at the level of documents” | claim_relations[3] |
| edge | topic —operationalised-as→ latent multinomial variables— “We refer to the latent multinomial variables in the LDA model as topics, so as to exploit text-oriented intuitions, but we make no epistemological claims regarding these latent variables beyond their utility in representing probability distributions on sets of words” | concept_edges[0] |
| edge | bag-of-words —same-as→ exchangeability— “All of these methods are based on the “bag-of-words” assumption—that the order of words in a document can be neglected. In the language of probability theory, this is an assumption of exchangeability for the words in a document (Aldous, 1985). Moreover, although less often stated formally, these methods also assume that documents are… | concept_edges[1] |
| note | bag-of-words —renamed-across-fields→ exchangeability — ADDS the better-typed edge beside the record's 'same-as'. The same-as is defensible and is left alone; this one says WHY they are the same - it is one assumption renamed between fields.— “All of these methods are based on the “bag-of-words” assumption—that the order of words in a document can be neglected. In the language of probability theory, this is an assumption of exchangeability for the words in a document (Aldous, 1985). Moreover, although less often stated formally, these methods also assume that documents are… | concept_edges[2] |
| relation | bag-of-words —renamed-across-fields→ exchangeability — ONE assumption under two field vocabularies - information retrieval calls it bag-of-words, probability theory calls it exchangeability— “All of these methods are based on the “bag-of-words” assumption—that the order of words in a document can be neglected. In the language of probability theory, this is an assumption of exchangeability for the words in a document (Aldous, 1985). Moreover, although less often stated formally, these methods also assume that documents are… | concept_edges[2] |
| outcome (stated by the tagger, no quotation) | control — held | controls[0] |
| for which use | limitation — every topic assignment over multi-word names and phrases: THE BAG-OF-WORDS ASSUMPTION ALLOWS WORDS THAT SHOULD BE GENERATED BY THE SAME TOPIC (E.G., 'WILLIAM RANDOLPH HEARST FOUNDATION') TO BE ALLOCATED TO SEVERAL DIFFERENT TOPICS— “In particular, the bag-of-words assumption allows words that should be generated by the same topic (e.g., “William Randolph Hearst Foundation”) to be allocated to several different topics.” | limitations[0] |
| for which use | limitation — the text-classification result: ALTHOUGH THESE RESULTS NEED FURTHER SUBSTANTIATION, THEY SUGGEST THAT THE TOPIC-BASED REPRESENTATION ... MAY BE USEFUL AS A FAST FILTERING ALGORITHM— “Although these results need further substantiation, they suggest that the topic-based representation provided by LDA may be useful as a fast filtering algorithm for feature selection in text classification.” | limitations[1] |
| for which use | limitation — any topic solution treated as the posterior: EXACT INFERENCE IS INTRACTABLE FOR LDA, and only approximate inference algorithms are available— “Exact inference is intractable for LDA, but any of a large suite of approximate inference algorithms can be used for inference and parameter estimation within the LDA framework.” | limitations[2] |
| for which use | limitation — any interpretation of a fitted topic as a theme or subject: the authors state they use the word topics to exploit text-oriented intuitions and make no epistemological claims about these latent variables beyond their utility in representing probability distributions on sets of words— “We refer to the latent multinomial variables in the LDA model as topics, so as to exploit text-oriented intuitions, but we make no epistemological claims regarding these latent variables beyond their utility in representing probability distributions on sets of words” | limitations[3] |
| for which use | limitation — any humanities reading of this model's output: the authors state the language of text collections is used to guide intuition and that the LDA model is not necessarily tied to text— “We use the language of text collections throughout the paper, referring to entities such as “words,” “documents,” and “corpora.” This is useful in that it helps to guide intuition, particularly when we introduce latent variables which aim to capture abstract notions such as topics. It is important to note, however, that the LDA model is not… | limitations[4] |
| for which use | limitation — any use of a topic model over a corpus with an order that matters, such as a diachronic study: the methods assume documents are exchangeable, so the specific ordering of documents in a corpus can be neglected, which the authors note is less often stated formally— “All of these methods are based on the “bag-of-words” assumption—that the order of words in a document can be neglected. In the language of probability theory, this is an assumption of exchangeability for the words in a document (Aldous, 1985). Moreover, although less often stated formally, these methods also assume that documents are… | limitations[5] |
| replication | untested | |
| limitation | The bag-of-words (exchangeability) assumption can split words that belong to a single phrase across different topics.— “In particular, the bag-of-words assumption allows words that should be generated by the same topic (e.g., “William Randolph Hearst Foundation”) to be allocated to several different topics.” | limitations[0] |
| limitation | The text-classification results are presented as preliminary and needing further support.— “Although these results need further substantiation, they suggest that the topic-based representation provided by LDA may be useful as a fast filtering algorithm for feature selection in text classification.” | limitations[1] |
| limitation | Exact posterior inference in LDA cannot be done; only approximate inference is available.— “Exact inference is intractable for LDA, but any of a large suite of approximate inference algorithms can be used for inference and parameter estimation within the LDA framework.” | limitations[2] |
| limitation | The authors disclaim any epistemological claim about the variables they call topics.— “We refer to the latent multinomial variables in the LDA model as topics, so as to exploit text-oriented intuitions, but we make no epistemological claims regarding these latent variables beyond their utility in representing probability distributions on sets of words” | limitations[3] |
| limitation | The text vocabulary is stated to be an aid to intuition, and the model is not tied to text.— “We use the language of text collections throughout the paper, referring to entities such as “words,” “documents,” and “corpora.” This is useful in that it helps to guide intuition, particularly when we introduce latent variables which aim to capture abstract notions such as topics. It is important to note, however, that the LDA model is not… | limitations[4] |
| limitation | Documents as well as words are assumed exchangeable, which the authors note is less often stated.— “All of these methods are based on the “bag-of-words” assumption—that the order of words in a document can be neglected. In the language of probability theory, this is an assumption of exchangeability for the words in a document (Aldous, 1985). Moreover, although less often stated formally, these methods also assume that documents are… | limitations[5] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | LDA is a generative probabilistic model for text corpora: a three-level hierarchical Bayesian model in which each item is a finite mixture over an underlying set of topics.— “latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite… | content_tags.findings[0] |
| finding | Inference uses efficient approximate variational methods and an EM algorithm; results in document modeling, text classification and collaborative filtering are compared with a mixture-of-unigrams model and probabilistic LSI. | content_tags.findings[1] |
| definition | latent Dirichlet allocation (LDA) | content_tags.definitions[0] |
| definition | topic Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[1] |
| definition | tf-idf scheme Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[2] |
| evidence status note | definition — QUOTE RECOVERED FROM THE PAPER'S OWN CHARACTERS. The recorded quote was LETTERS-ONLY IDENTICAL to this stretch of the staged text but not an exact substring of it - spaces, hyphens, ligature glyphs or mathematical operators differed, so it could never anchor. This is the file's own text between the same two letters: not produced by a rule about the damage, copied out of the file. Round-trip checked (the recovered span is letters-only equal to the original) and length-checked. The text as originally recorded is kept in evidence as extracted. | content_tags.definitions[2] |
| key-terms | topic, document, corpus, mixture, probabilistic, words, generative, Dirichlet, variational |
| concept | latent multinomial variables Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[4] |
| concept | exchangeability Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[5] |
| concept | bag-of-words Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[6] |
| concept | perplexity Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[7] |
| concept | topic Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[8] |
| Bayesian | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[0] |
| evidence status note | Bayesian — QUOTE REPAIRED: the extractor split a unicode and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted. | content_tags.statistics[0] |
| mean μ | as written: mean of Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[1] |
| claim type | method — a second reading reaching the same value; recorded: method Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | alternatives.claim_type |
| method | probabilistic-modelling — a second reading DIFFERING from the recorded value; recorded: system-or-tool-building Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | alternatives.method |
| phenomenon | latent topic structure in collections of discrete data such as text corpora — a second reading DIFFERING from the recorded value; recorded: topic modelling of document collections Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | alternatives.phenomenon |
When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.
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