Michael P' Oakes

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Michael P. Oakes

• University of Sunderland

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Contents

• Proposals for a Masters programme in Natural
  Language Processing
• Future research plans / link with Wolverhampton
• Plans for publications
• Plans for grant proposals
• Other funding ideas

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Proposals for a Masters programme in Natural
Language Processing

• Some preliminaries
• Entry requirements first or second class degree
  in a related discipline. Computer programming
  will be taught from scratch.
• Funding Erasmus, European Social Fund, ESRC
  Masters training package scheme for programme
  development, work-based learning
• Students must receive an accurate idea of the
  content of the programme beforehand
• Induction week meet the teaching team,
  familiarity with the University, formal
  registration, etc.
• Diploma, Certificate and Masters awards. 8
  taught modules (24 lectures, 18 hours practical,
  58 directed reading, 50 self-directed research).

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Certificate Stage
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Diploma Stage
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Project

• Close links with industry established through
  3-month industrial placements, based either with
  the company or at the University.
• The sponsor will either be from industry or
  academia, and there will also be a staff member
  from Wolverhampton to act as supervisor.
• Project management (TOR, reviews), poster, viva,
  dissertation (typically introduction, research,
  analysis, implementation, evaluation /
  experiments, reflective conclusions).

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Administration

• Programme board of studies Institute Director or
  deputy, student representatives, one or more
  employers representatives, module leaders,
  programme leader, responsible for the management
  of the programme and the well-being of each
  module.
• Board of assessment to decide student
  progression. External Examiner, no student
  representatives
• Internal (prior to hand-out) and External (sample
  work shown prior to programme assessments)
  moderation.
• Other quality control student and staff
  feedback, EEs report, programme annual report.
• Each student has a personal tutor and student
  handbook.
• Timely, face-to-face assessment may improve
  student satisfaction.

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Future Research Plans,

• And how these might complement the research
  topics of the Research Group in Computational
  Linguistics.

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Automatic Summarisation

• CAST Project produced an automatic summarisation
  tool term-based summarisation
• Content-Based Abstracting (Paice).
• TRESTLE (Gaizauskas).
• David Evans evaluation of information extraction
• Query-based summaries. Intrinsic
  (representativeness) vs. Extrinsic (judgeability)
  evaluation (Liang).
• SumTrain reached second round of EU evaluation.
• Extraction of statistics-related phrases, e.g.
  greater than, significant reduction in, was
  directly proportional to, did not affect.

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Concept-Based Abstracting Project

• window length 4
• STOP 6 "and foliar treatment AGEN"
• 5 "foliar treatment AGEN "
• 5 "treatment AGEN AGEN"
• 4 "effect of mildew AGEN"
• 3 "AGEN gave a significant"
• 2 "AGEN was the most"
• 2 "AGEN at different sowing"
• 2 "AGEN increased fertile tillers
• LOW-FQ 1 "effect of AGEN sprays"

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Automatic Terminology Processing

• Le An Ha looked at the concept of a terminology
  rather than individual terms. Knowledge patterns
  from glossaries store of terms and relations
  between them.
• David Evans. Identification of terms using TF.IDF
  and other statistical methods (see slide 20).
• Shiyan Ou. Sentiment classification (see slide
  20).
• Constantin Orasan. Corpus of junk mail (spam
  filters, Farrow).
• Constantin Orasan. Analysis of genre differences
  project on Language, Computation and Style
  (authorship).
• Englishes, Scrip newsfeeds, BELGA feature
  extraction for text classification.

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Annotation tools

• Constantin Orasan PALinkA, automatic annotation
  of anaphoric links.
• Lewandowska, Oakes Rayson part-of-speech and
  semantic code tagging in English alignment
  enables partial semantic tagging of L2.

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Annotation Aligned and Partially Tagged Polish
text (Lewandowska, Oakes and Rayson)

• Tak jest_A3 mowi Polemarch_Z99 a do_Z5 tego
  jeszcze urzadra nocne nabozenstwo, ktore_Z8 warto
  zobaczyc
• __PUNC That_DD1_Z8 s_VBZ_A3 the_AT_Z5
  way_NN1_X4.2 of_IO_Z5 it_PPH1_Z8 ,_,_PUNC
  __PUNC said_VVD_Q2.1 Polymarchus_NP1_Z99
  _,_,PUNC __PUNC and_CC_Z5 ,_,_PUNC
  besides_RR_Z5 _,_,PUNC there_EX_Z5, is_VBZ_A3
  to_TO_Z5 be_VBI_A3 a_AT1_Z5 night_NNT1_T1.3
  festival_NN1_K1/S1.1.3 which_DDQ_Z8
  will_VM_T1.1.3 be_VBI_A3 worth_II_I1.3
  seeing_VVG_X3.4 ._._PUNC

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Mobile Devices

• Laura Hasler and Dalila Mekhaldi QALL-ME,
  Question-Answering for Digital Phones.
• Chufeng Chen Annotation of digital photographs
  taken with a GPS camera. A gazetteer
  translated longitude and latitude data into
  place name, geographical feature, e.g. Long
  54.91, Lat -1.4, place Sunderland, feature
  harbour. Episodic memory.

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Other Related Work

• Andrea Mulloni Corpus Linguistics.
• Empirical vs. Chomskyan
• Own interest Statistics for Corpus Linguistics.
• Driving the process rather than merely testing
  for statistical significance, e.g. Mutual
  Information to find collocations.
• Irina Temnikova Machine Translation
• Alignment for example-based machine translation
  (Lewandowska Oakes).

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Plans for Publications (1)

• Book Chapters in press
• Processing Multilingual Corpora, Chapter 32 of
  Corpus Linguistics An International Handbook,
  eds. Anke Lüdeling and Merja Kytö, Mouton de
  Gruyter.
• Corpus Linguistics and Stylometry, Chapter 52,
  ibid.
• Corpus Linguistics and Language Variation, in
  Contemporary Approaches to Corpus Linguistics,
  ed. Paul Baker, Continuum.
• Javanese, in Languages of the World, ed.
  Bernard Comrie, Routledge.
• J. Vilares, M. Oakes and M. Vilares A
  Knowledge-Light Approach to Query Translation in
  CLIR. RANLP V, ed. N. Nicolov, Benjamins.

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Plans for Publications (2)

• Under second review
• S-W. Ke, C. Bowerman and M. Oakes, Automatic
  classification of personal email with PERC and
  time-related strategies, ACM Transactions on
  Information Systems.
• W-C Lin, M. Oakes and J. Tait, Improving image
  annotation via representative feature selection,
  Cognitive Processing.

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Plans for Publications (3)

• Future plans
• VITALAS Video and image Indexing and reTrievAl in
  the LArge Scale.
• Update Statistics for Corpus Linguistics sold
  over 1500 copies, but now 10 years old
• Last chapter was Literary Detective Work, which
  could be a book in its own right disputed
  authorship (compendium of techniques,
  Shakespeare, religious texts, still unsolved
  mysteries e.g. The Quiet Don, Marxism and the
  Philosophy of Language), unknown languages
  (Linear B, Voynich manuscript). JLLC, QL.

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Plans for Grant Proposals (1)

• Closing the Semantic Gap
• Related to machine learning (boosting), caption
  analysis, gazetteers, alignment of low level
  image content features and high level semantic
  features (words)
• Son of VITALAS?

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Plans for Grant Proposals (2)

• Which words are truly characteristic of a corpus?
  X² etc.
• Countable linguistic features.
• Measures from IR e.g. PageRank (Lódz, Palomino).
• AHRC (if theoretical, Englishes), ESRC (if
  applied, e.g. spam filters).
• Sentiment analysis (Thijs Westerveld at Teezir)
  mining online opinions. Cheerful, chic, cheap,
  clean vs. chaos, cranky, cumbersome, damaged.
• Interface between NLP and IR sentence analysis
  e.g. adjectives, negatives follow links to
  navigate websites.
• IR relevant vs. irrelevant documents.

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Plans for Grant Proposals (3)

• Temporal relations in query language modelling
  (Dawei Song).
• Temporal similarity semantic similarity ?
  overall similarity.
• The temporal similarity between texts (e.g. query
  and document) can be estimated by a) time stamp,
  b) temporal logic between the texts (Andrea
  Setzer).

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Plans for Grant Proposals (4)

• Corpus Profiling Workshop on October 18th.
• Exploring how corpus characteristics affect the
  behaviour of techniques in IR and NLP, and to set
  out a roadmap for a shared research agenda.
• Data set profile impacts on automatic
  classification, IR, anaphora resolution,
  automatic summarisation and word sense
  disambiguation.

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Other Funding Ideas

• IRSG-like Industry Day to foster industrial
  contacts (consultancy? Grant proposals?)
• Organise conferences, e.g. bid for Corpus
  Linguistics, CLEF, ECIR.
• Exploitation of Intellectual Property.
• Is there an equivalent of CEDEC (Computing and
  Engineering Distance Education Centre) with whom
  we can discuss marketing programmes world-wide /
  part-time? Work-based learning?