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Networks and Natural Language Processing

Networks and Natural Language Processing



This presentation is a briefing of a paper about Networks and Natural Language Processing. It describes many graph based methods and algorithms that help in syntactic parsing, lexical semantics and ...

This presentation is a briefing of a paper about Networks and Natural Language Processing. It describes many graph based methods and algorithms that help in syntactic parsing, lexical semantics and other applications.



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    Networks and Natural Language Processing Networks and Natural Language Processing Presentation Transcript

    • Networks and NLP Networks and Natural Language Processing Presented by: Ahmed Magdy Ezzeldin
    • Graphs in NLP● Graphs are used in many NLP applications like : - Text Summarization - Syntactic parsing - Word sense disambiguation - Ontology construction - Sentiment and subjectivity analysis - Text clustering● Associative or semantic networks are used torepresent the language units and their relationswhere language units are the vertices (nodes) andthe relations are the edges (links).
    • Networks are Graphs Nodes are Vertices Links are Edges- Node can represent text units can be : (words,collocations, word senses, sentences,documents)- Graph nodes do not have to be of the samecategory- Edges can represent relations: (co-occurrence,collocation, syntactic dependency, lexicalsimilarity)
    • Outline● Syntax 1- Dependency Parsing 2- Prepositional Phrase Attachment 3- Co-reference Resolution● Lexical Semantics 1- Lexical Networks 2- Semantic Similarity and Relatedness 3- Word Sense Disambiguation 4- Sentiment and Subjectivity Analysis● Other Applications 1- Summarization 2- Semi-supervised Passage Retrieval 3- Keyword Extraction
    • Syntax
    • 1- Dependency Parsing An approach to sentence parsing Dependency tree of a sentence is a directed subgraph of the full graph connecting all words in the sentence. So this subgraph is a tree The root of the tree is the main predicate that takes arguments which are the child nodes
    • ● (McDonald et al, 2005) made a parser thatfinds the tree with the highest score using CLE(Chu Liu Edmonds) Algorithm of Maximumspanning tree (MST) in a directed graph.● Each node picks the neighbor with the highestscore which will lead to a spanning tree or a cycle● CLE collapses each cycles into a single node● CLE runs in O(n^2)
    • ●No tree covers all nodes so the closest 2 nodesare collapsed
    • ● We repeat this step untilall nodes are collapsedthen an MST is constructedby reversing the procedureand expanding all nodes.● McDonald achievedexcellent results on astandard English data setand even better results onCzech (free word orderlanguage)
    • 2- Prepositional Phrase Attachment● (Toutanova et al., 2004) A preposition like "with" is eitherattached to the main predicate (high verbal attachment) or thenoun phrase before it (low nominal attachment).- “I ate pizza with olives.”- “I ate pizza with a knife.”● He proposed a semi-supervised learning process where agraph of nouns and verbs is constructed and if 2 wordsappear in the same context they are connected with an edge.● Random walk until convergence●Reached performance of 87.54% classification accuracywhich is near the human performance which is 88.20%
    • 3- Co-reference Resolution● Identifying relations between entityreferences in a text● Can be nouns or pronouns● Approximate the correct assignment ofreferences to entities in a text by using a graph-cut algorithm. Method:A graph is constructed for each entity● Every entity is linked to all the possible co-reference with weighted edges where weightsare the confidence of each co-reference.● Min-cut partitioning separate each entity and itsco-references.
    • Lexical Semantics Semantic Analysis, Machine Translation, Information retrieval, question answering, knowledge acquisition, word sense disambiguation, semantic role labeling,textual entailment, lexical acquisition, semantic relations
    • 1- Lexical Networksa- Unsupervised lexical acquisition (Widdows andDorow, 2002)Goal: build semantic classes automatically from rawcorporaMethod:● Build a co-occurrence graph from British NationalCorpus where nodes are words linked by conjunction(and/or)● Over 100,000 nodes and over half a million edges.● Representative nouns are manually selected and put ina seed set.● Largest number of links with the seed set is added tothe seed
    • Result:Accuracy 82% which isfar better than beforeThe drawback of thismethod is low coverageas it is limited to words inconjunction relation only.
    • 1- Lexical Networks [continued]b- Lexical Network Properties (Ferrer-i-Cancho andSole, 2001)Goal:● Observe Lexical Networks propertiesMethod:● Build a co-occurrence network where words arenodes that are linked with edges if they appear in thesame sentences with distance of 2 words at most.● Half million nodes with over 10 million edgesResult:● Small-world effect: 2-3 jumps can connect any 2 words● Distribution of node degree is scale-free
    • 2- Semantic Similarity and Relatedness●Methods include metrics calculated on existingsemantic networks like WordNet by applying shortestpath algorithms to identify the closest semantic relationbetween 2 concepts (Leacock et al. 1998)● Random Walk algorithm (Hughes and Ramage, 2007)● PageRank gets the stationary distribution of nodes inWordNet biased on each word of an input word pair.● Divergence between these distributions is calculated toshow the words relatedness.
    • 3- Word Sense Disambiguationa- Label Propagation Algorithm (Niu et al. 2005)Method:● Construct a graph of labeled and unlabeled examples for agiven ambiguous word● Word sense examples are the nodes and weighted edges aredrawn by pairwise metric of similarity.● Known labeled examples are the seed set are assigned withtheir correct labels (manually)● Labels are propagated through the graph through the weightededges● Labels are assigned with certain probability● The propagation is repeated until the correct labels areassigned.Result: Performs better than SVM when there is a small numberof examples provided.
    • b- Knowledge-based word sensedisambiguation(Mihalcea et al.2004, Sinha andMihalcea 2007)
    • Method:● Build a graph for a given text and all the senses of itswords as nodes● Senses are connected on the basis of their semanticrelations (synonymy, antonymy ...)● A random walk results in a set of scores that reflects theimportance of each word sense.Result:● Superior to other Knowledge-based word sensedisambiguation that did not use graph based representations.Follow up work:● Mihalcea did not use semantic relations but she usedweighted edges using a measure of lexical similarity● Brought generality as it can use any electronic dictionarynot just a semantic network like WordNet
    • c- Comparative Evaluation of Graph ConnectivityAlgorithms (Navigli and Lapata, 2007)● Applied on word sense graphs derived from WordNET●Found out that the best measure to use is a closenessmeasure
    • 4- Sentiment and Subjectivity Analysisa- Using min-cut graph algorithm (Pang and Lea 2004)Method:● Drawing a graph where sentences are the nodes and theedges are drawn according to the sentences proximity● Each node is assigned a score showing the probability that itssentence is subjective using a supervised subjectivity classifier● Use min-cut algorithm to separate subjective from objectivesentences.Results:● Better than the supervised subjectivity classifierb- By Assignment subjectivity and polarity labels (Esuli andSebastiani 2007)Method:● Random walk on a graph seeded with nodes labeled forsubjectivity and polarity.
    • Other Applications
    • 1- Summarizationa- (Salton et al. 1994, 1997)● Draw a graph of the corpus where every node is a paragraph● Lexically similar paragraphs are linked with edges● A summary is retrieved by following paths defined by differentalgorithms to cover as much of the content of the graph aspossible.b- Lexical Centrality (Erkan and Radev 2004) (Mihalcea andTarau 2004)Method:● Sentences are nodes of the graph● Random walk to define the most visited nodes as central tothe documents● Remove duplicates or near duplicates● Select sentences with maximal marginal relevance
    • 2- Semi-supervised Passage Retrieval●Question Biased Passage Retrieval(OtterBacher et al., 2005)Answer a question from a group of documentsMethod:● Use biased random walk on a graph seededwith positive and negative examples● Each node is labeled according to thepercentage a random walk ends at this node● The nodes with the highest score are central tothe document set and similar to the seed nodes.
    • 3- Keyword Extraction●A set of terms thatbest describes thedocument●Used in terminologyExtraction andconstruction ofdomain specificdictionaries
    • ● Mihalcea and Tarau, 2004Method:● Build a co-occurrence graph of for the input text wherewords are the the text words● Words are linked by co-occurrence relation limited bythe distance between words.● Random walk on graph● Words ranked as important important and found nextto each other are collapsed into one key phraseResult:● A lot better than tf.idf
    • References Networks and Natural Language Processing (Mihalcea and Radev 2008) Dragomir Radev University of Michigan radev@umich.edu Rada Mihalcea University of North Texas rada@cs.unt.edu