Matching needs and resources: How NLP can help theoretical - - PowerPoint PPT Presentation

Matching needs and resources: How NLP can help theoretical linguistics

Alexis Dimitriadis

Utrecht institute of Linguistics OTS Utrecht University

Motivation

• Insight: Linguistic research could be carried out

more efficiently (or: with better results) if all that NLP horsepower could be brought to bear. But how?

• Some linguists have both theoretical and NLP

knowledge, and can choose the techniques to apply. But most theoretical linguists are not self-sufficient in this respect:

• Theoretical linguists should want help from NLP

.

• Computational linguists should want to help.

How NLP could help I

Best-case scenario

• Collaboration, to tackle a research question

interesting to both sides. Examples: Language genealogy, learning algorithms (e.g., learning OT constraint ranking).

• However: suitable research problems are limited.
• Many theoretical projects have modest (i.e., boring)

computational needs. A large corpus of English is enough for a lot of linguists...

Some collaborative research topics

• Computational cladistics for Indo-European language
• families. (Ringe, Warnow and Taylor 2002)
• Algorithms for learning constraint rankings for

Optimality-Theoretic systems (phonology, stress, syntax...) (Tesar and Smolensky 2000)

• Cognitive modeling of various linguistic phenomena

(cf. Workshop 5)

• Studying the semantics of lexical entailments, using a

new custom-developed corpus. (Winter et al.,

• ngoing)

How NLP could help II

Lending a helping hand

• Freebies Use of existing corpora, parsers etc.
• Altruism The computational linguist undertakes to

help the theorist. Examples: Linguist’s Search Engine (Resnik et al. 2005), Natural Language Toolkit (Bird et al. 2009). Problems: Existing resources are often hard to use for the uninitiated. Altruism is a limited resource. How is it most effective?

How NLP could help III

Learning from NLP methodology

• Data-driven orientation
• Reproducibility, inter-annotator consistency
• Testing against a corpus of data
• Evaluation metrics

Utilizing concrete resources

and technical stumbling blocks

• Many existing, boring tools and resources would be

useful to a theoretical linguist: corpora, parsers, web crawlers...

• However, linguists typically lack the necessary

technical know-how, compilers, or even knowledge that such tools exist.

The Linguist’s Search Engine

Resnik and Elkiss (2005)

• Searching the web by word, POS and syntactic
• structure. Search engine results are parsed and

filtered with enriched queries.

• Stored core corpus for fast results; supplemented

with real-time crawling and parsing if desired.

• User-friendly, easy to use web interface, designed for

the theoretical linguist. Graphical query-by-example query construction.

The Linguist’s Search Engine

Resnik and Elkiss (2005)

• Searching the web by word, POS and syntactic
• structure. Search engine results are parsed and

filtered with enriched queries.

• Stored core corpus for fast results; supplemented

with real-time crawling and parsing if desired.

• User-friendly, easy to use web interface, designed for

the theoretical linguist. Graphical query-by-example query construction.

• Defunct. Too much work to create and support such

special tools?

The Natural Language Toolkit

Bird et al. (2009)

• A collection of Python modules for text analysis and

various NLP tasks

• Interactive command-line environment for interactive

linguistic exploration

• Relatively little technical skill required
• Documented in a very accessible book (Bird et al.

2009) targeted to the “ordinary working linguist”

Benefits of the no-frills approach

• Command line tools are easier to write and maintain.

Also better for scripting, creating workflows, etc.

• An integrated tool is less flexible in the tasks it can

carry out. A little scripting unlocks the power of computational techniques.

• Command line tools still need to be reasonably easy

to install and use, and should be well documented at an accessible level.

Benefits of the no-frills approach

• Command line tools are easier to write and maintain.

Also better for scripting, creating workflows, etc.

• An integrated tool is less flexible in the tasks it can

carry out. A little scripting unlocks the power of computational techniques.

• Command line tools still need to be reasonably easy

to install and use, and should be well documented at an accessible level.

• The NLTK and similar resources will still be beyond

the reach of linguists unable, or unwilling, to make the required time investment.

• Is this a big problem? I don’t believe it is.

Conclusions I

• To benefit from NLP

, theoretical linguists must incorporate its methods and values in their work habits.

• Joint research is the best scenario, but is limited in
• reach. Many needs are simpler.
• Tool creation is not a strength of theoretical linguists.

Making resources available is great– and the tools don’t need to be point-and-click.

Conclusions II

• We theoretical linguists need to help ourselves, by

adopting methodological insights and learning to use the associated techniques and tools.

• Hopefully, computationally savvy theorists will go on

to formulate more research questions suitable for real collaboration with computational linguists.