Distributional models Katrin Erk Representing meaning through collections

Distributional models Katrin Erk

Representing meaning through collections of words Doc 1: Abdullah boycotting challenger commission dangerous election interest Karzai necessary runoff Sunday Doc 3: applications documents engines information iterated library metadata precision query statistical web Doc 2: animatronics are children’s igloo intimidating Jonze kingdom smashing Wild Doc 4: cucumbers crops discoveries enjoyable fill garden leafless plotting Vermont you

Representing meaning through collections of words Wikipedia (version Oct Washington Post Oct 24, 2009 on elections in 1: Abdullah Afghanistan Doc boycotting challenger commission dangerous election interest Karzai necessary runoff Sunday Doc 3: applications documents engines information iterated library metadata precision query statistical web Wikipedia (version Oct 24, 2009) on Information Retrieval 24, 2009) on the movie “Where the Wild Things Are” Doc 2: animatronics are children’s igloo intimidating Jonze kingdom smashing Wild Doc 4: cucumbers crops discoveries enjoyable fill garden leafless plotting Vermont you Planning a garden. org: Vegetable Garden

Representing meaning through a collection of words What parts of the meaning of a document can you capture through an unordered collection of words? How can you make use of such collections?

Representing meaning through a collection of words What parts of the meaning of a document can you capture through an unordered collection of words? General topic information: What is the document about? More specifically: things mentioned in the document How can you make use of such collections? Documents on similar topics contain similar words Use in Information Retrieval (search)

Representing collections of words through tables of counts Doc 2: animatronics are children’s igloo intimidating Jonze kingdom smashing Wild film wild max that things as [edit] him jonze released 24 18 16 16 12 12 11 9 9 6

Representing collections of words through tables of counts We can now compare documents by comparing tables of counts. What can you tell about the second document below? film wild max that things as [edit] him jonze released 24 18 16 16 12 12 11 9 film wild max that things as [edit] him jonze released 17 0 0 9 0 36 8 7 9 0 6 3

The “second document”: a more extensive list of words the 167 and 58 of 58 to 56 a 49 in 37 as 36 is 33 victor 30 * 27 with 26 by 23 her 18 film 17 for 16 emily 15 was 15 corpse 14 bride 13 victoria 13 his 13 on 13 from 11 What movie is this?

From tables to vectors film wild max that things as [edit] him jonze released 24 18 16 16 12 12 11 9 9 6 Interpret table as a vector: Each entry is a dimension: “film” is a dimension. Document’s coordinate: 24 “wild” is a dimensions. Document’s coordinate: 18 … Then this document is a point in 10 -dimensional space

Documents as points in vector space Viewing “Wild Things” and “Corpse Bride” as vectors/points in vector space: Similarity between them as proximity in space Where the Wild Things Are Corpse Bride “Distributional model”, “vector space model”, “semantic space model” used interchangeably here

What have we gained? Representation of document in vector space can be computed completely automatically: Just counts words Similarity in vector space is a good predictor for similarity in topic Documents that contain similar words tend to be about similar things

What do we mean by “similarity” of vectors? Euclidean distance (a dissimilarity measure!): Where the Wild Things Are Corpse Bride

What do we mean by “similarity” of vectors? Cosine similarity: Where the Wild Things Are Corpse Bride

What have we gained? We can compute the similarity of documents through their Euclidean distance or through their cosine We can also represent a query as a vector: Just count the words in the query Now we can search for documents similar to the query

From documents to words Same holds for words as for documents: Context words are a good indicator of meaning Similar words tend to occur in similar contexts What is a context? How do we count here? Take all the occurrences of our target word in a large text Take a context window, e. g. 10 words either side Count all that occurs there

Representing the meaning of a word through a collection of context words Emerging from the earth is Emily, the "Corpse Brid a beautiful undead girl in a moldy bridal gown who declares Victor her husband. a the corpse emerging from 2 2 1 1 1 is undead beautiful moldy bride 1 1 1 in earth girl 1 1 1 Counts for target “Emily”, 10 words context either side.

Representing the meaning of a word through a collection of context words a the corpse emerging from 2 2 1 1 1 is undead beautiful moldy bride 1 1 1 in earth girl 1 1 1 Go through all occurrences of “Emily” in a large corpus Count words in 10 -word window for each occurrence, sum up

Some co-occurrences: “letter” in “Pride and Prejudice” jane : 12 • when : 14 • by : 15 • which : 16 • him : 16 • with : 16 • elizabeth : 17 • but : 17 • he : 17 • be : 18 • s : 20 • on : 20 not : 21 for : 21 mr : 22 this : 23 as : 23 you : 25 from : 28 i : 28 had : 32 that : 33 in : 34 was : 34 it : 35 his : 36 This is not a her : 50 large text! a : 52 Large = and : 56 something like of : 72 100 million to : 75 words at least she : 41 the : 102

From tables to vectors Counts for “letter” and “surprise” from Pride and Pr Interpret table as a vector: Each entry is a dimension: “admirer” is a dimension. Coordinate of “letter”: 1. Coordinate of “surprise”: 0 “all” is a dimensions. Coordinate of “letter”: 8. Coordinate of “surprise: 7 … Then each word is a point in n-dimensional space

What have we gained? Representation of word in vector space can be computed completely automatically: Just counts co-occurring words in all context Similarity in vector space is a good predictor for meaning similarity Words that occur in similar contexts tend to be similar in meaning Synonyms are close together in vector space Antonyms too

Parameters of vector space models W. Lowe (2001): “Towards a theory of semantic space” A semantic space defined as a tuple (A, B, S, M) B: base elements. A: mapping from raw co-occurrence counts to something else, to correct for frequency effects S: similarity measure. M: transformation of the whole space to different dimensions

B: base elements We have seen: context words as base elements Term x document matrix: Represent document as vector of weighted terms Represent term as vector of weighted documents

B: base elements Dimensions: not words in a context window, but dependency paths starting from the target word (Pado & Lapata 07)

A: transforming raw counts Problem with vectors of raw counts: Distortion through frequency of target word Weigh counts: The count on dimension “and” will not be as informative as that on the dimension “angry” For example, using Pointwise Mutual Information between target a and context word b

M: transforming the whole space Dimensionality reduction: Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Latent Semantic Analysis, LSA (also called Latent Semantic Indexing, LSI): Do SVD on term x document representation to induce “latent” dimensions that correspond to topics that a document can be about Landauer & Dumais 1997

Using similarity in vector spaces Search/information retrieval: Given query and document collection, Use term x document representation: Each document is a vector of weighted terms Also represent query as vector of weighted terms Retrieve the documents that are most similar to the query

Using similarity in vector spaces To find synonyms: Synonyms tend to have more similar vectors than non-synonyms: Synonyms occur in the same contexts But the same holds for antonyms: In vector spaces, “good” and “evil” are the same (more or less) So: vector spaces can be used to build a thesaurus automatically

Using similarity in vector spaces In cognitive science, to predict human judgments on how similar pairs of words are (on a scale of 1 -10) “priming”

An automatically extracted thesaurus Dekang Lin 1998: For each word, automatically extract similar words vector space representation based on syntactic context of target (dependency parses) similarity measure: based on mutual information (“Lin’s measure”) Large thesaurus, used often in NLP applications

Vectors for word senses Up to now: one vector per word Vector for “bank” conflates financial contexts fishing contexts How to get to vectors for word senses?

Automatically inducing word senses Schütze 1998: one vector per sentence, or per occurrence (token)of “letter” She wrote an angry letter to her niece. He sprayed the word in big letters. The newspaper gets 100 letters from readers every day. Make token vector by adding up the vectors of all other (content) words in the sentence: Cluster token vectors Clusters = induced word senses

A vector for an individual occurrence of a word Avoid having to define word senses Sometimes hard to divide uses into senses: words like “leave”, or “paint” Erk/Pado 2008: Modify vector of “bank” using its syntactic context: break obj ban k fish on ban k

Summary: vector space models Representing meaning through counts Represent document through content words Represent word meaning through context words / parse tree snippets / documents Context items as dimensions, target as vector/point in semantic space Proximity in semantic space ~ similarity between words

Summary: vector space models Uses: Search Inducing ontologies Modeling human judgments of word similarity Represent word senses Cluster sentence vectors Compute vectors for individual occurrences