TF-IDF

It's a family of weighting schemes for the Vector Space Model in Information Retrieval and Text Mining


Vector Space Model

Suppose we have

  • a collection of documents $\mathcal D = \{ d_1, \ ... \ , d_N \}$ with $N$ documents
  • the vocabulary $V = \{ w_1, \ ... \ , w_M \}$ consisting of $M$ words
  • then each document is represented by $d = (x_1, x_2, \ ... \ , x_M)$ where $x_i$ is the weight assigned to the word $w_i$ in the document $d \in \mathcal D$
  • let $D$ be a matrix with rows indexed by documents and columns indexed by words. $D$ is called a Document-Term matrix


Term Weighing Systems

Weights can be:

  • binary: 1 if term is present and 0 if not
  • term frequency (TF): frequency of each word in a document
  • document frequency (DF): words that are used more in the collections have more weight
  • TF-IDF: combination of sublinear TF and inverse document frequency


Term Frequency

Term Frequency (TF)

  • local frequency of a word in the document
  • i.e. the word is weighed by how many times it occurs in the document
  • $\text{tf}(w, d) = \big| \{ w' \in d \ : \ w' = w \} \big|$ where $w$ is a word and $d = \{ w_1, \ ... \ , w_m \}$ is a document


Sublinear TF:

  • sometimes a word is used too often so we want to reduce its influence compared to other less frequently used words
  • for that we can use some sublinear function, e.g.
  • $\log \text{tf}(w, d)$ or $\sqrt{\text{tf}(w, d)}$


Document Frequency

Document Frequency (DF)

  • global frequency of a word in the document collection
  • it's the number of documents that contain the word:
  • $\text{df}(w, \mathcal D) = \big| \{ d \in \mathcal D \ : \ w \in d \} \big|$ where $w$ is a word and $\mathcal D = \{ d_1, \ ... \ , d_N \}$ is the document corpus


Inverse Document Frequency (IDF)

  • more often we're interested in words that are rare across the document collections
  • they tend to be domain specific and are usually more relevant for retrieving this document
  • so we should give them more weight than to high-frequency words
  • thus, $\text{idf}(w, \mathcal D) = \log \cfrac{ |\mathcal D| + 1 }{\text{df}(w, \mathcal D)}$
  • also can be some Entropy-based measure


Good Weighting System

The main function of a weighting scheme is to enhance IR effectiveness

  • want to retrieve items relevant to the users
  • and non-relevant items should be rejected


Effectiveness of an IR system is measures via Precision and Recall

  • precision $P$: % of retrieved items that are relevant
  • recall $R$: % of relevant items retrieved
  • want both $P$ and $R$ be good
  • $R$ is best served by broad high-frequency items: such terms are frequent and are likely to contain relevant documents
  • $P$ best served by narrow and specific terms: the ones with "discriminating power"
  • we can't achieve both, so need to compromise: use terms broad enough to get good $R$, but without producing very low $P$


Suggestions:

  • TF should be good for getting high frequency words
  • but using just TF is not good: if high frequency words are contained in many documents, all of them will be retrieved, thus affecting precision
  • thus need a collection dependent factor that favors terms that are contained in fewer documents: IDF
  • IDF varies inversely with the # of documents and the number of documents containing them


Term discrimination considerations:

  • best terms are the ones that can distinguish individual documents from the test
  • so best terms should have high TF but low IDF => so we can combine them and use TF $\times$ IDF


TF-IDF Scheme

When weighting we want to get:

  • domain specific words
  • words that are frequent in the document
  • this can be done by combining TF and IDF:
  • use sub-linear TF to avoid the dominating effect of words that occur very frequently
  • use IDF to reduce weights of terms that occur more frequently to ensure that document matching is done with more discriminative words
  • as the result, terms appearing too rarely or too frequently are ranked low


Intuition:

  • the more often a term occurs in a document, the more representative it is of this document
  • the more documents contain a term, the lest discriminating it becomes


So, we can combine then my multiplying:

  • $\text{tf-idf}(w, d \mid \mathcal D) = (1 + \log \text{tf}(w, d)) \cdot \log \cfrac{|\mathcal D|}{\text{df}(w, \mathcal D)}$
  • this is often used in Text Mining, but in Information Retrieval there can be other components in the TF-IDF


Normalization

In systems where vectors have very different lengths a third component of a weight can be useful:

  • Term normalization component


Why?

  • if we have a short document $d$, then for corresponding vector $d$, $\| d \|$ is small
  • if we have a large doc with many words, then $\| d \|$ is big
  • so for larger documents the chances of matching are higher => so larger documents have higher chances of being retrieved just because they are larger


Normalization Factor

  • Thus, we need to incorporate the normalization factor $\cfrac{d}{\| d \|}$
  • It's also called "Cosine Normalization": we normalize weights s.t. they have a unit length
  • then for a document vector $d$ and a query vector $q$, $\| d \| = 1$ and $\| q \| = 1$
  • if we do this, the Inner Product is the same as Cosine Similarity: $d^T q = \text{cosine}(d, q)$


Pivoted Length Normalization Function

In Information Retrieval more involved variants of TF-IDF give better performance

  • they account for TF, IDF and also document length
  • but it's still a TF-IDF variation


for example,

  • $$\text{tf-idf}(d, q \mid \mathcal D) = \sum_{w \in q, d} = \cfrac{1 + \ln \big(1 + \ln \text{tf}(w, d) \big)}{(1 - s) + s \cdot \| d \| / \| \bar d \|} \cdot \text{tf}(w, q) \cdot \ln \cfrac{\| \mathcal D\| + 1}{\text{df}(w, \mathcal D)}$$
  • where $s$ - some parameter
  • $\| d \|$ is the length of document $d$, i.e. how many words are in $d$
  • $\| \bar d \|$ is the average document length


Critique


Smoothing vs TF-IDF

Smoothing and TF-IDF are connected


Sources

  • Information Retrieval (UFRT)
  • Zhai, ChengXiang. "Statistical language models for information retrieval." 2008.
  • Salton, Gerard, and Christopher Buckley. "Term-weighting approaches in automatic text retrieval." 1988. [2]
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