Alexander Gelbukh www.Gelbukh.com

Special Topics in Computer ScienceAdvanced Topics in Information RetrievalLecture 11: Natural Language Processing and IR. Semanticsand Semantically-rich representations Alexander Gelbukh www.Gelbukh.com

Previous Lecture: Conclusions • Syntax structure is one of intermediaterepresentations of a text for its processing • Helps text understanding • Thus reasoning, question answering, ... • Directly helps POS tagging • Resolves lexical ambiguity of part of speech • But not WSD-type ambiguities • A big science in itself, with 50 (2000?) years of history

Previous Lecture: Research topics • Faster algorithms • E.g. parallel • Handling linguistic phenomena not handled bycurrent approaches • Ambiguity resolution! • Statistical methods • A lot can be done

Contents • Semantic representations • Semantic networks • Conceptual graphs • Simpler representations • Head-Modifier pairs • Tasks beyond IR • Question Answering • Summarization • Information Extraction • Cross-language IR

Syntactic representation A sequence of syntactic trees.

Semantic analysis Semanticanalysis

Semantic representation Complex structure of whole text

Semantic representation • Expresses the (direct) meaning of the text • Not what is implied • Free of the means of communications • Morphological cases (transformed to semantic links) • Word order, passive/active • Sentences and paragraphs • Pronouns (resolved) • Free of means of expressing • Synonyms (reduced to a common ID) • Lexical functions

Lexical Functions • The same meaning expressed by different words • The choice of the word is a function of other words • Few standard meanings • Example: Magn = “much”, “very” • Strong wind, tea, desire • Thick soup • High temperature, potential, sea; highly expensive • Hard work; hardcore porno • Deep understanding, knowledge, appreciation

“give” pay attention provide help adjudge a prize yield the word confer a degree deliver a lection “get” attract attention obtain help receive a degree attend a lection ...Lexical Functions

...Syntagmatic lexical functions • In semantic representation, are transformed to the function name: • Magn wind, tea, desire • Magn soup • Magn temperature, potential, sea; MAGN expensive • Magn work; Magn porno • Magn understanding, knowledge, appreciation • In different languages, different words are used... • Russian: dense soup; Spanish: loaded tea, lend attention • ...but the same function names.

Example: Translation

...Paradigmatic lexical functions • Used for synonymic rephrasing • Need to reduce the meaning to a standard form • Example: Syn, hyponyms, hypernyms • W  Syn (W) • complex apparatus  complex mechanism • Example: Conv31, Conv24, ... • A V B C C Conv31(V) B A • John sold the book to Mary for $5 • Mary bough the book from John for $5 • The book costed Mary $5

Semantic network • Representation of the text as a directed graph • Nodes are situations and entities • Edges are participation of an entity in a situation • Also situation in a situation:begin reading a book, John died yesterday • Situation can be expressed with a noun:Professor delivered a lection to studentsProfessor “*lectured” to studentsLecture on history, memorial to heroes • A node can participate in many situations! • No division into sentences

Situations • Situations with different participants are different situations • John reads a book and Mary reads a newspaper. He aks her whether the newspaper is interesting. • Here two different situations of reading! • But the same entities: John, Mary, newspaper, participating in different situations • Tense and number is described as situations • John reads a book: • Now (reading (John, book) & quantity (book, one)

Semantic valencies • A situation can have few participants (up to ~5) • Their meaning is usually very general • They are usually “naturally” ordered: • Who (agent) • What (patient, object) • To whom (receiver) • With what (instrument, ...) • John sold the book to Mary for $5 • So, in the network the outgoing arcs of a node are numbered

Semantic representation Complex structure of whole text Now Give 2 1 ATTENTION Now GOVERNMENT 1 IMPORTANT 2 Now COUNTRY 1 2 2 Possess SCIENCE Quantity 1 1 WE

Reasoning and common-sense info • One can reason on the network • If John sold a book, he does not have it • For this, additional knowledge is needed! • A huge amount of knowledge to reason • A 9-year-old child knows some 10,000,000 simple facts • Probably some of them can be inferred, but not (yet) automatically • There were attempts to compile such knowledge manually • There is a hope to compile it automatically...

Semantic representation ... and common-sense knowledge

Computer representation • Logical predicates • Arcs are arguments • In AI, allows reasoning • In IR, can allow comparison even without reasoning

Conceptual Graphs • A CG is a bipartite graph. • Concept nodes represent entities, attributes, or events (actions). • Relation nodes denote the kinds of relationships between the concept nodes. • [John](agnt)[love](ptnt)[Mary]

pnt analyze mnr logically program:{*} provide ptn criteria for ptn Invariant:{*} use ptn examine of Implication:{*} approach for for diagnosis attr of correction attr error logical automatic

Use in IR • Restrict the search to specific situations • Where John loves Mary, but not vice versa or • Soften the comparison • Approximate search • Look for John loves Mary, get someone loves Mary

Graph Generation Tagging TEXTS Parsing CGs Obtaining from text • “Algebraic formulation of flow diagrams” • Algebraic|JJ formulation|NN of|IN flow|NN diagrams|NNS • [[np, [n, [formulation, sg]], [adj, [algebraic]], [of, [np, [n, [diagram, pl]], [n_pos, [np, [n, [flow, sg]]]]]]]] • [algebraically](manr)[formulate](ptn)[flow-diagram]

Steps of comparison • Determine the common elements (overlap) between the two graphs. • Based on the CG theory • Compatible common generalizations • Measure their similarity. • The similarity must be proportional to the size of their overlap.

An overlap • Given two conceptual graphs G1 and G2, the set of their common generalizationsO = {g1, g2,...,gn} is an overlap if: • If all common generalizations giare compatible. • If the set O is maximal.

(a) candidate:Bush criticize candidate:Gore G1: candidate:Bush criticize candidate:Gore G1: Agnt Agnt Agnt Agnt Agnt Ptnt Ptnt Ptnt Ptnt Ptnt candidate criticize O1: candidate Candidate:Bush criticize O2: Candidate:Gore candidate:Gore criticize candidate:Bush G2: candidate:Gore criticize candidate:Bush G2: (b) An example of overlap

Similarity measure • Conceptual similarity: indicates the amount of information contained in common concepts of G1 and G2. • Do they mention similar concepts? • Relational similarity: indicates how similar the contexts of the common concepts in both graphs are. • Do they mention similar things about the common concepts?

Conceptual similarity • Analogous to the Dice coefficient. • Considers different weights for the different kinds of concepts. • Considers the level of generalization of the common concepts (of the overlap).

Relational Similarity • Analogous to the Dice coefficient. • Considers just the neighbors of the common concepts. • Considers different weights for the different kinds of conceptual relations.

Similarity Measure • Combines the conceptual and relational similarities. • Multiplicative combination: a similarity roughly proportional to each of the two components. • Relational similarity has secondary importance: even if no common relations exits, the pieces of knowledge are still similar to some degree.

Flexibility of the comparison • Configurable by the user. • Use different concepthierarchies. • Designate the importance for the different kind of concepts. • Manipulate the importance of the conceptual and relational similarities.

Example of the flexibility Gore criticezes Bush vs. Bush criticizes Gore

An Experiment • Use the collection CACM-3204 (articles of computer science). • We built the conceptual graphs from the document titles. • Query: Description of a fast procedure for solving a systemof linear equations.

The results • Focus on the structural similarity, basically on the one caused by the entities and attributes. • (a=0.3,b=0.7, We=Wa=10,Wv=1) • One of the best matches: • Description of a fast algorithm for copying list structures.

The results (2) • Focus on the structural similarity, basically on the one caused by the entities and actions. • (a=0.3,b=0.7, We=Wv=10,Wa=1) • One of the best matches: • Solution of an overdetermined system of equations in the L1 norm.

Advantages of CGs • Well-known strategies for text comparison (Dice coefficient) with new characteristics derived from the CGs structure. • The similarity is a combination of two sources of similarity: the conceptual similarity and the relational similarity. • Appropriate to compare small pieces of knowledge (other methods based on topical statistics do not work). • Two interesting characteristics: uses domain knowledge and allows a direct influence of the user. • Analyze the similarity between two CGs from different points of view. • Selects the best interpretation in accordance with the user interests.

Simpler representations • Head-Modifier pairs • John sold Mary an interesting book for a very low price • John sold, sold Mary, sold book, sold for priceinteresting book, low price • A paper in CICLing-2004 • Restrict your semantic representation to only two words • Shallow syntax • Semantics improves this representation • Standard form: Mary bought  John sold, etc.

Tasks beyond IR: Question Answering • User information need • An answer to a question • Not a bunch of docs • Who won Nobel Peace Prize in 1992? (35500 docs)

...QA • Answer: Rigoberta Menchú Tum • Logical methods: • “Understand” the text • Reason on it • Construct the answer • Generate the text expressing it • Statistical methods (no or little semantics) • Look what word is repeated in the docs • Perhaps try to understand something around it

...Better QA • What is the info is not in a single document? • Who is the queen of Spain? • King of Spain is Juan Carlos • Wife of Juan Carlos is Sofía • (Wife of a king is a queen) • Logical reasoning may prove useful • In practice, the degree of “understanding” is not yet enough • We are working to improve it

Tasks beyond IR: Passage Extraction • If the answer is long: a story • What do you know on wars between England and France? • Or if we cannot detect the simple answer • Then find short pieces of the text where the answer is • Can be done even with keywords: • Find passages with many keywords • (Kang et al. 2004): Choose passages with greatest vector similarity. Too short: few keywords, too long: normalized • Awful quality  • Reasoning can help

Tasks beyond IR: Summarization • And what if the answer is not in a short passage • Summarize: say the same (without unimportant details) but in fewer words • Now: statistical methods • Reasoning can help

Tasks beyond IR: Information Extraction • Question answering on a massive basis • Fill a database with the answers • Example: what company bought what company and when? • A database of three columns • Now: (statistical) patterns • Reasoning can help

Cross-lingual IR • Question in one language, answer in another language • Or: question and summary of the answer in English, over a database in Chinese • Is a kind of translation, but simpler • Thus can be done more reliably • A transformation into semantic network can greatly help

Research topics • Recognition of the semantic structure • Convert text to conceptual graphs • All kinds of disambiguation • Shallow semantic representations • Application of semantic representations to specifictasks • Similarity measures on semantic representations • Reasoning and IR

Semantic representation gives meaning Language-specific constructions used only in theprocess of communication are removed Network of entities / situations and predicates Allows for translation and logical reasoning Can improve IR: Compare the query with the doc by meaning, not words Search for a specific situation Search for an approximate situation QA, summarization, IE Cross-lingual IR Conclusions

Thank you! Till June 15? 6 pm Thesis presentation? Oral test?

Alexander Gelbukh www.Gelbukh.com