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Text Stream Processing

Dunja Mladenić

Marko Grobelnik

Blaž Fortuna

Delia Rusu

Artificial Intelligence Laboratory

Jožef Stefan Institute

Ljubljana, Slovenia

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• What are text streams

• Properties of text streams

• Motivation

• Pre-processing of text streams

• Text quality

Introduction to text streams

Introduction to text streams

• Topic detection

• Entity, event and fact extraction and resolution

• Word sense disambiguation

• Summarization

• Sentiment analysis

• Social network analysis

Text stream processing Text stream processing • Key literature overview

• Further publicly available tools

• Conclusions

• Questions and discussion

Concluding remarks

Concluding remarks

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Introduction to Text Streams

What are text streams

Properties of text streams

Motivation

Pre-processing of text streams

Text quality

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What are data streams

Continuously arriving data, usually in real-time

Dealing with streams can be often easy, but… …gets hard when we have an intensive data stream and complex operations on data are required!

In such situations usually… …the volume of data is too big to be stored

…the data can be scanned thoroughly only once

…the data is highly non-stationary (changes properties through time), therefore approximation and adaptation are key to success

Therefore, a typical solution is… …not to store observed data explicitly, but rather in the aggregate form which allows execution of required operations

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Stream processing

Who works with real time data processing?

“Stream Mining” (subfield of “Data Mining”) dealing

with mining data streams in different scenarios in

relation with machine learning and data bases

http://en.wikipedia.org/wiki/Data_stream_mining

“Complex Event Processing” is a research area

discovering complex events from simple ones by

inference, statistics etc.

http://en.wikipedia.org/wiki/Complex_Event_Processing

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Why one would need (near) real-time information

processing?

…because Time and Reaction Speed correlate with

many target quantities – e.g.:

…on stock exchange with Earnings

…in controlling with Quality of Service

…in fraud detection with Safety, etc.

Generally, we can say: Reaction Speed == Value

…if our systems react fast, we create new value!

Motivation for stream processing

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What are text streams

Continuous, often rapid, ordered sequence of texts

Text information arriving continuously over time in

the form of a data stream

News and similar regular report

News articles, online comments on news, online traffic

reports, internal company reports, web searches,

scientific papers, patents

Social media

discussion forums (eg., Twitter, Facebook), short

messages on phones or computer, chat, transcripts of

phone conversations, blogs, e-mails

Demo http://newsfeed.ijs.si

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NewsFeed

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Properties of text streams

Produced with a high rate over time

Can be read only once or a small number of times

(due to the rate and/or overall volume)

Challenging for computing and storage

capabilities – efficiency and scalability of the

approaches

Strong temporal dimension

Modularity over time and sources (topic,

sentiment,…)

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Example task: evolution of research topics and communities over time

Based on time stamped research publication titles and

authors

Observe which topics/communities shrunk, which

emerged, which split, over time, when in time were the

turning points,…

TimeFall – monitoring dynamic, temporally evolving

graphs and streams based on Minimum Description

Length

find good cut-points in time, and stitch together the communities:

good cut-point leads to shorter description length.

fast and efficient incremental algorithm, scales to large datasets,

easily parallelizable

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Example task: evolution of research topics and communities over time

Given: n time-stamped events (eg., papers), each related to

several of m items (eg., title-words, and/or author-names)

Find cluster patterns and summarize their evolution in time

V

1 2

3

45

Papers WordsTime

1990

1990

1991

1990

1992

1991

1992

1991

Time

1992

1990

1990

1992

1991

1991

1991

1990

Words

Pa

pe

rs

Words

1990

1991

1992

Time

1990

1991

1992

Time Word Clusters

1990

1991

1992

Time Word Clusters

1990

1992

Time Word Clusters

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TimeFall on 12 million

medical publications

from PubMed

MEDLINE over 40

years

scales linearly with the

product of the initial

time point blocks and

the number of non-

zeros in the matrix

J. Ferlez, C. Faloutsos, J. Leskovec, D. Mladenic, M. Grobelnik. Monitoring Network Evolution

using MDL. International Conference on Data Engineering (ICDE 2008).

J. Ferlez, C. Faloutsos, J. Leskovec, D. Mladenic, M. Grobelnik. Monitoring Network Evolution

using MDL. International Conference on Data Engineering (ICDE 2008).

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Pre-processing text stream

Basic text pre-processing

including removing stop-words, applying stemming

Representing text for internal processing

Splitting into units (eg., sentences or words)

Mapping to internal representation (eg., feature

vectors of words, vectors of ontology concepts)

Pre-processing for aligning/merging text streams

Time wise alignment of multiple text streams -

coordinated text streams (appearing over the same

time window, eg. news)

Content alignment possibly over different languages

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Example

The city hosts a great number of religious

buildings, many of them dating back to

medieval times.

Stop Words

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Example

city hosts great number religious buildings,

many them dating back medieval times.

Stemming

host religi build

date time mediev

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Example

city host great number religi build, many

them date back mediev time.

Splitting into units of words

(city, host, great, number, religi, build,

many, them, date, back, mediev, time)

Feature vector of words

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Text Quality

Factors:

Vocabulary use

Grammatical and fluent sentences

Structure and coherence

Non-redundant information

Referential clarity – e.g. proper usage of pronouns

Models of text quality

Global coherence - overall document organization

Local coherence - Adjacent sentences

Language model based approaches

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• What are text streams

• Properties of text streams

• Motivation

• Pre-processing of text streams

• Text quality

Introduction to text streams

Introduction to text streams

• Topic detection

• Entity, event and fact extraction and resolution

• Word sense disambiguation

• Summarization

• Sentiment analysis

• Social network analysis

Text stream processing Text stream processing • Key literature overview

• Further publicly available tools

• Conclusions

• Questions and discussion

Concluding remarks

Concluding remarks

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Text Stream Processing

WEB

Web

Crawler

Web

Crawler

Text Pre-

Processing

Text Pre-

Processing

Topic

Detection

Topic

Detection

Information

Extraction

Information

Extraction

Word

Sense

Disambiguation

Word

Sense

Disambiguation

Summarization Summarization

Sentiment

Analysis

Sentiment

Analysis

Social

Network

Analysis

Social

Network

Analysis

Text Stream

Processing Results

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Topic Detection

Religion

Art

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Topic Detection

Supervised techniques

The data is labeled with predefined topics

Machine learning algorithms are used to predict

unseen data labels

Unsupervised techniques

Identify patterns and structure within the dataset

Clustering: grouping data sharing similar topics

Statistical methods: probabilistic topic modeling

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Probabilistic Topic Modeling

Topic: a probability distribution over words in a

fixed vocabulary

Given an input corpus containing a number of

documents, each having a sequence of words,

the goal is to find useful sets of topics

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Latent Dirichlet Allocation

D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of

Machine Learning Research, 3:993–1022, January 2003

D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of

Machine Learning Research, 3:993–1022, January 2003

Documents

can have

multiple topics

Religion

Art

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LDA Generative Process

A topic is a distribution over words

A document is a mixture of topics (at the level of the corpus)

Each word is drawn from one of the corpus-level topics

For each document generate the words: 1. Randomly choose a distribution over the topics

2. For each word in the document a) Randomly choose a topic from the distribution over topics in

(step 1)

b) Randomly choose a word from the corresponding distribution over the vocabulary

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LDA Generative Process

religious 0.03

monastery 0.01

church 0.01

art 0.02

painter 0.02

sculpture 0.01

park 0.01

garden 0.01

Assume a number of

topics for the

document collection

(Craiova guide)

Choose a distribution

over the topics

For each word:

• Choose a topic

assignment

• Choose the word

from the topic

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Topic Models - Extensions

Hierarchical Topic Models

D. Blei, T. Griffiths, and M. Jordan. The nested

Chinese restaurant process and Bayesian

nonparametric inference of topic hierarchies. Journal

of the ACM, 57:2 1–30, 2010.

Q. Ho, J. Eisenstein, E. P. Xing. Document

Hierarchies from Text and Links. WWW 2012

Dynamic Topic Models

D. Blei and J. Lafferty. Dynamic topic models. In

Proceedings of the 23rd International Conference on

Machine Learning, 2006.

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Topic Detection in Streams

Unsupervised methods

Simpler approaches – e.g. Clustering

Probabilistic topic models

Challenging because of the amount and dynamics of

the data

E.g. Online inference for LDA – fits a topic model to

random Wikipedia articles

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Topic Detection Tools

Available implementations

LDA, HLDA, …

http://www.cs.princeton.edu/~blei/topicmodeling.html

Mallet

Toolkit for statistical NLP

http://mallet.cs.umass.edu/

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Clustering on text streams

Grouping similar documents – adjusting to changes in the topics over time Clusters generated as the data arrives and stored in a tree

Adding examples by adjusting the whole path from the root to the leaf node with the new example – adding, removing, splitting and merging clusters

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Clustering on Reuters V1 news (colors showing predefined topics)

B. Novak, Algorithm for identifying topics in text streams, 2008 B. Novak, Algorithm for identifying topics in text streams, 2008

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Topic Detection - DEMOS

A 100-topic browser of the dynamic topic model

fit to Science (1882-2001)

http://topics.cs.princeton.edu/Science/

Browsing search results

http://searchpoint.ijs.si/

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100-topic browser Science (1882-2001)

1890 1940 2000

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Search Point

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Entity Extraction

Subtask of information extraction

Identifying elements in text which belong to a

predefined group of things:

Names of people, locations, organizations (most

common)

Time expressions, quantities, money amounts,

percentages

Gene and protein names

Etc.

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Entity Extraction

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Entity Extraction Approaches

Lists of entities (gazetteers) and grammar rules

e.g. GATE – General Architecture for Text

Engineering

H. Cunningham, et al. Text Processing with GATE (Version

6). University of Sheffield Department of Computer Science.

15 April 2011

Statistical models

e.g. Stanford NER - linear chain Conditional Random

Field (CRF) sequence models

J. R. Finkel, T. Grenager, and C. Manning. Incorporating Non-

local Information into Information Extraction Systems by

Gibbs Sampling. In ACL 2005, pp. 363-370.

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Collective Entity Resolution

Entity resolution: discover and map entities to corresponding references (e.g from a database, knowledge base, etc.).

Approaches: Pairwise similarity with attributes of references

Relational clustering using both attribute and relational information

I. Bhattacharya, L. Getoor. Collective entity resolution in relational data. ACM Transactions on Knowledge Discovery from Data (TKDD), 2007.

Topic models for the context of every word in a knowledge base

P. Sen. Collective Context-Aware Topic Models for Entity Disambiguation. WWW 2012

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Entity Resolution to Linked Data

Enhance named entity classification using Linked Data

features

Y. Ni, L. Zhang, Z. Qiu, C. Wang. Enhancing the Open-Domain

Classification of Named Entity using Linked Open Data. ISWC,

2010.

Type knowledge base from LOD

(name string, type)

E.g. from the triplet (dbpedia:Craiova, rdf:type, Place) ->

(Craiova, Place)

Uses WordNet as an intermediate taxonomy to compute

the similarity between the LOD type and the target type

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Entity Resolution to Linked Data

Finding all possible forms under which an entity

can occur in text

Resource descriptions - most useful rdfs:label and

foaf:name

Redirect relationship

(entity1, type1)

(entity2, ?)

entity1 has URI1

entity2 has URI2

URI1 owl:sameAs URI2

Conclude: (entity2, type1)

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Relation Extraction

Identifying relationships between entities (and more generally phrases)

Traditional relation extraction The target relation is given, together with corresponding extraction patterns for the relation

A specific corpus

Open relation extraction (and more general Open information extraction)

Diverse relations, not previously fixed

Corpus: the Web

M. Banko, O. Etzioni. The Tradeoffs Between Open and Traditional Relation Extraction. ACL, 2008.

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Identifying Relations for Open IE

3-step method:

Label – automatically label sentences with extractions

(arg1, relation phrase, arg2)

Learn – learn a relation phrase extractor (e.g using CRF)

Extract – given a sentence, identify (arg1, arg2) and the

relation phrase (based on the learned relation extractor)

Examples

TextRunner – M. Banko, M. Cafarella, S. Soderland, M.

Broadhead, O. Etzioni. Open Information Extraction from

the Web. IJCAI 2007.

WOE – F. Wu, D.S. Weld. Open Information Extraction

using Wikipedia. ACL 2010.

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Identifying Relations for Open IE

REVERB

Input: POS-tagged and NP-chunked sentence

Identify relation phrases

syntactic and lexical constraints

Find a pair of NP arguments for each relation

phrase – assign confidence score (logistic

regression classifier)

Output: (x,y,z) extraction triplets

A. Fader, S. Soderland O. Etzioni. Identifying Relations for Open

Information Extraction. EMNLP 2011.

A. Fader, S. Soderland O. Etzioni. Identifying Relations for Open

Information Extraction. EMNLP 2011.

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Identifying Relations for Open IE

Key points:

Relation phrases are identified holistically as

opposed to word-by-word

Potential phrases are filtered based on statistics

(lexical constraints)

relation first opposed to arguments first

relation phrase not confused for arguments (e.g. “made a deal

with”)

DEMO

http://openie.cs.washington.edu/#

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REVERB

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Never Ending Language Learning

NELL – Never Ending Language Learning

Addressed tasks

Reading task: read the Web and extract a knowledge base of

structured facts and knowledge.

Learning task: improved (and updated) reading – extract

past information more accurately

A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E.R. Hruschka Jr. and T.M. Mitchell.

Toward an Architecture for Never-Ending Language Learning. AAAI, 2010.

A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E.R. Hruschka Jr. and T.M. Mitchell.

Toward an Architecture for Never-Ending Language Learning. AAAI, 2010.

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Never Ending Language Learning

A. Carlson et al. 2010

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Never Ending Language Learning

Coupled Pattern Learner (CPL) Extracts instances of categories and relations (using contextual patterns)

Coupled SEAL (CSEAL) Queries the Web with beliefs from each category or relation, mines lists and tables to extract new instances

Coupled Morphological Classifier (CMC) One regression model per category – classifies noun phrases

Rule Learner (RL) Infer new relation instances

DEMO http://rtw.ml.cmu.edu/rtw/

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NELL

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Domain and Summary Templates

Domain templates Event-centric: the focus is on events described with verbs

E. Filatova, V. Hatzivassiloglou, K. McKeown. Automatic creation of domain templates. In Proceedings of COLING/ACL 2006

Summary templates Entity-centric: the focus is on summarizing entity categories

P. Li, J. Jiang, Y. Wang. Generating templates of entity summaries with an entity-aspect model and pattern mining. ACL 2010.

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Domain Templates

Domain is a set of events of a particular type

E.g. presidential elections, football championships

Domains can be instantiated – instances of

events of a particular type

E.g. Euro Championship 2012

Different levels of granularity

Hierarchical structure for domains

Template – a set of attribute-value pairs

The attributes specify functional roles characteristic

for the domain events

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Domain Templates

Use a corpus describing instances of events within a domain and learn the domain templates (general characteristics of the domain)

The verbs are used as a starting point – estimate of the verb importance given the domain

The sentences containing the top X verbs are parsed

The most frequent subtrees (FREQuent Tree miner) are kept

The named entities are substituted with more generic constructs – e.g. POS tags

The frequent sub-trees are merged together

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Domain Templates

E.g. terrorist attack domain

• Important verbs

Killed, told, found, injured, reported,

Happened, blamed, arrested, died, linked

• Frequent subtrees

(VP(ADVP(NP))(VBD killed)(NP(CD 34)(NNS people)))

(VP(ADVP)(VBD killed)(NP(CD 34)(NNS people)))

• Merging subtrees

(VBD killed)(NP(NUMBER)(NNS people))

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Summary Templates

Starting point: a collection of entity summaries for

a given entity category

Goal: to obtain a summary template for the entity

category

E.g. The physicist category

ENTITY received his phd from ? university

ENTITY studied ? under ?

ENTITY earned his ? in physics from university of ?

ENTITY was awarded the medal in ?

ENTITY won the ? award

ENTITY received the Nobel prize in physics in ?

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Summary Templates

Identify subtopics (aspects) of the summary

collection

Using LDA (see Topic Detection)

Each word:

a stop word,

a background word,

a document word,

an aspect word

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Summary Templates

Sentence patterns are generated for each aspect

frequent subtree pattern mining

Fixed structure of a sentence pattern

Aspect words, background words, stop words

Template slots – vary between documents

Document words

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Summary Templates

Sentence pattern generation

Locate subject entities (using heuristics) – e.g.

pronouns in a biography

Generate parse trees (using Stanford Parser) – label

stop, background, aspect, document, entity words

given by the topic model

Mine frequent subtree patterns (using FREQT)

Prune patterns without entities or aspect words

Convert subtree patterns to sentence patterns (find

the sentences that generated the pattern)

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Word sense disambiguation

Identifying the meaning of words in context

Supervised WSD Words labeled with their senses are required

Classification task

Unsupervised WSD Known as word sense induction

Clustering task

Knowledge-based WSD Relies on knowledge resources: WordNet, Wikipedia, OpenCyc, etc.

R. Navigli. Word sense disambiguation: A survey.

ACM Computational Surveys, 41(2), 2009.

R. Navigli. Word sense disambiguation: A survey.

ACM Computational Surveys, 41(2), 2009.

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Word Sense Disambiguation

Ponzetto, S.P. and Navigli, R. Knowledge-rich

Word Sense Disambiguation Rivaling Supervised

Systems. ACL 2010.

Extend WordNet with Wikipedia relations

Apply simple knowledge-based approaches

Performance was similar with state-of-the-art

supervised approaches

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WSD Evaluation

Evaluation workshops SenseEval, SemEval, …

WSD evaluation topics (SemEval 2010) Cross-lingual WSD

WSD on a specific domain

Word sense induction

Disambiguating Sentiment Ambiguous Adjectives

Evaluation topics related to WSD (SemEval 2012)

Semantic textual similarity – similarity between two sentences

Relational similarity – between pairs of words

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Summarization

Extractive

Identifying relevant sentences that belong to the

summary

Abstractive

Identifying/paraphrasing sections of the document to

be summarized

E.g. Summarization as phrase extraction - K.

Woodsend, M. Lapata. Automatic Generation of Story

Highlights. ACL, 2010.

joint content selection and compression model

ILP model to determine phrases that form the highlights

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Summarization Evaluation

Several evaluation workshops

Document Understanding Conferences (DUC), Text

Analysis Conferences (TAC)

Metrics: ROUGE (n-gram based)

Linguistic quality

Grammaticality, non-redundancy, referential clarity,

focus, structure and coherence

E. Pitler, A. Louis, A. Nenkova. Automatic Evaluation

of Linguistic Quality in Multi-Document

Summarization. ACL 2010.

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Sentiment analysis

Broad sense: sentiment analysis ~ opinion mining

“computational treatment of opinion, sentiment, and subjectivity in text” (B. Pang, L. Lee, 2008)

Surveys, book chapters: B. Pang, L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), pp. 1–135, 2008

B. Liu. Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Second Edition, (editors: N. Indurkhya and F. J. Damerau), 2010.

B. Liu. Web Data Mining - Exploring Hyperlinks, Contents and Usage Data, Ch. 11: Opinion Mining, Second Edition, Springer, 2011.

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Interactive Approach to Sentiment Analysis

http://aidemo.ijs.si/render/index.html Model and task

selection

Query result, grouped by

predicted label

Examples retrieved by

uncertainty or class-margin

sampling

Query

Model-based filters

T. Stajner and I. Novalija. Managing Diversity through Social Media, ESWC

2012 Workshop on Common value management.

T. Stajner and I. Novalija. Managing Diversity through Social Media, ESWC

2012 Workshop on Common value management.

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Architecture

Indexed documents

Active learning for

modeling topic,

sentiment (diversity

analysis)

Interactive user

interface

Example: stream of social

media posts relevant to

brand management

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Social Network Analysis

Modeling social

relationships

Network theory

concepts

Nodes – individuals

within the network

Edges – relationships

between individuals

Mario Karlovčec, Dunja Mladenić, Marko Grobelnik, Mitja Jermol. Visualizations of

Business and Research Collaboration in Slovenia, Proc. Of the Information

Technology Interfaces 2012.

Mario Karlovčec, Dunja Mladenić, Marko Grobelnik, Mitja Jermol. Visualizations of

Business and Research Collaboration in Slovenia, Proc. Of the Information

Technology Interfaces 2012.

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Influence and Passivity in Social Media

Majority of Twitter users are passive information consumers – do not forward the content to the network

Influence and passivity based on information forwarding activity

Passivity User retweeting rate and audience retweeting rate

how difficult it is for other users to influence him

Algorithm ~ HITS Passivity score ~ authority score

Most passive: robot users – follow many users, but retweet a small percentage

Influence score ~ hub score Most influential: news services – post many links forwarded by other users

D.M. Romero, W. Galuba, S. Asur, and B.A. Huberman. Influence and

Passivity in Social Media. ECML PKDD, 2011.

D.M. Romero, W. Galuba, S. Asur, and B.A. Huberman. Influence and

Passivity in Social Media. ECML PKDD, 2011.

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• What are text streams

• Properties of text streams

• Motivation

• Pre-processing of text streams

• Text quality

Introduction to text streams

• Topic detection

• Entity, event and fact extraction and resolution

• Word sense disambiguation

• Summarization

• Sentiment analysis

• Social network analysis

Text stream processing • Key literature overview

• Further publicly available tools

• Conclusions

• Questions and discussion

Concluding remarks

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Key literature

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Further publicly available tools

Topic detection David Blei’s homepage: http://www.cs.princeton.edu/~blei/topicmodeling.html

Mallet: http://mallet.cs.umass.edu/

Natural language toolkits GATE: http://gate.ac.uk/

OpenNLP: http://opennlp.apache.org/

Nltk: http://nltk.org/

Entity Extraction Stanford NER: http://nlp.stanford.edu/ner/index.shtml

Relation Extraction NELL: http://rtw.ml.cmu.edu/rtw/

REVERB: http://openie.cs.washington.edu/

WSD WordNet::SenseRelate: http://senserelate.sourceforge.net/

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Conclusions

Dealing with streams can be often easy, but… …gets hard when we have an intensive data stream and complex operations on data are required!

Topic detection Currently online inference (e.g. for LDA) is a new direction

Entity, relationship and template extraction, sentiment analysis and social network analysis

Are already applied on streams

Word Sense Disambiguation Complex knowledge bases (e.g. WordNet + Wikipedia) coupled with simple disambiguation algorithms work well

Summarization Abstraction summaries are more suited for text streams

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Questions and discussion