Line 41: | Line 41: | ||

* source of picture: [http://slidewiki.org/print/deck/339] | * source of picture: [http://slidewiki.org/print/deck/339] | ||

* sorting - because it's easier to take union: just merge the posting list | * sorting - because it's easier to take union: just merge the posting list | ||

+ | |||

+ | |||

+ | |||

+ | == Pairwise Similarity in Document Collection == | ||

+ | === [[Relation Database]] === | ||

+ | Suppose we have a table Documents(doc_id, <u>word</u>, weight) | ||

+ | * we index it on word: an have an inverted index | ||

+ | * then document-document similarity would be a self-join of Document with itself | ||

+ | |||

+ | |||

+ | === [[MapReduce]] === | ||

+ | How do we use it to efficiently compute pair-wise similarity between each document | ||

+ | * given two documents $\mathbf d_1$, $\mathbf d_2$: | ||

+ | * $\text{sim}(\mathbf d_1, \mathbf d_2) = \sum_{t \in V} = w_{t, \mathbf d_i} \cdot w_{t, \mathbf d_j} = \sum_{t \in \mathbf d_i \cup \mathbf d_j} = w_{t, \mathbf d_i} \cdot w_{t, \mathbf d_j} $ | ||

+ | * so we need to take into account only terms that both documents share | ||

+ | |||

+ | |||

+ | If we compute similarity for all documents: | ||

+ | * if a term $t$ appears only in documents $\mathbf x, \mathbf y, \mathbf z$, then it contributes only to the similarity scores between $(\mathbf x, \mathbf y), (\mathbf x, \mathbf z)$ and $(\mathbf y, \mathbf z)$ | ||

+ | |||

+ | |||

+ | |||

+ | Algorithm: | ||

+ | * let $\text{posting}(t)$ be a function that returns all documents that contain $t$ | ||

+ | * set $\text{sim}[i, j] = 0$ be the similarity matrix | ||

+ | * for $t \in V$ do: | ||

+ | ** $p_t = \text{posting}(t)$ | ||

+ | ** for all pairs $(\mathbf d_i, \mathbf d_j) \in (p_t \times p_t)$ (s.t. $i > j$) | ||

+ | ** $\text{sim}[i, j] = \text{sim}[i, j] + w_{t, \mathbf d_i} \cdot w_{t, \mathbf d_j}$ | ||

+ | |||

+ | |||

+ | It can easily be implemented in [[MapReduce]] | ||

+ | * first MR job: build the index | ||

+ | * second MR job: compute pair-wise similarity | ||

Line 47: | Line 81: | ||

* [[Information Retrieval (UFRT)]] | * [[Information Retrieval (UFRT)]] | ||

* Ertöz, Levent, Michael Steinbach, and Vipin Kumar. "Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data." 2003. [http://static.msi.umn.edu/rreports/2003/73.pdf] | * Ertöz, Levent, Michael Steinbach, and Vipin Kumar. "Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data." 2003. [http://static.msi.umn.edu/rreports/2003/73.pdf] | ||

− | + | * Elsayed, Tamer, Jimmy Lin, and Douglas W. Oard. "Pairwise document similarity in large collections with MapReduce." 2008. [[http://www.ece.umd.edu/~oard/pdf/acl08elsayed2.pdf]] | |

[[Category:Information Retrieval]] | [[Category:Information Retrieval]] | ||

[[Category:Database Indexes]] | [[Category:Database Indexes]] |

In Databases, indexing is needed to speed up queries

- want to avoid full table scan
- same is true for Information Retrieval and other Text Mining/NLP tasks
- Inverted index is a way of achieving this, and it can be generalized to other forms of input, not just text

For IR

- index is a partial representation of a document that contains the most important information about the document
- usually want to find terms to index automatically

Idea:

- usually a document contains only a small portion of terms
- so document vectors are very sparse
- typical distance is cosine similarity - it ignores zeros. for cosine to be non-zero, two docs need to share at least one term
- $D^T$ is the inverted index of the term-document matrix $D$

This, to find docs similar to $d$:

- for each $w_i \in d$
- let $D_i = \text{index}[w_i] - d$ be a set of documents that contain $w_i$ (except for $d$ itself)

- then take the union of all $D_i$
- calculate similarity only with documents from this union

Can be used in Document Clustering to speed up similarity computation

Build a dictionary: a "posting" list

- for each word we store ids of documents that have this word
- document are sorted by ids
- source of picture: [1]
- sorting - because it's easier to take union: just merge the posting list

Suppose we have a table Documents(doc_id, __word__, weight)

- we index it on word: an have an inverted index
- then document-document similarity would be a self-join of Document with itself

How do we use it to efficiently compute pair-wise similarity between each document

- given two documents $\mathbf d_1$, $\mathbf d_2$:
- $\text{sim}(\mathbf d_1, \mathbf d_2) = \sum_{t \in V} = w_{t, \mathbf d_i} \cdot w_{t, \mathbf d_j} = \sum_{t \in \mathbf d_i \cup \mathbf d_j} = w_{t, \mathbf d_i} \cdot w_{t, \mathbf d_j} $
- so we need to take into account only terms that both documents share

If we compute similarity for all documents:

- if a term $t$ appears only in documents $\mathbf x, \mathbf y, \mathbf z$, then it contributes only to the similarity scores between $(\mathbf x, \mathbf y), (\mathbf x, \mathbf z)$ and $(\mathbf y, \mathbf z)$

Algorithm:

- let $\text{posting}(t)$ be a function that returns all documents that contain $t$
- set $\text{sim}[i, j] = 0$ be the similarity matrix
- for $t \in V$ do:
- $p_t = \text{posting}(t)$
- for all pairs $(\mathbf d_i, \mathbf d_j) \in (p_t \times p_t)$ (s.t. $i > j$)
- $\text{sim}[i, j] = \text{sim}[i, j] + w_{t, \mathbf d_i} \cdot w_{t, \mathbf d_j}$

It can easily be implemented in MapReduce

- first MR job: build the index
- second MR job: compute pair-wise similarity

- Information Retrieval (UFRT)
- Ertöz, Levent, Michael Steinbach, and Vipin Kumar. "Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data." 2003. [2]
- Elsayed, Tamer, Jimmy Lin, and Douglas W. Oard. "Pairwise document similarity in large collections with MapReduce." 2008. [[3]]