Package: align
ALIGN, a computational tool for multi-level language analysis (optimized for Python 3.10)alignis a Python library for extracting quantitative, reproducible
metrics of multi-level alignment between two speakers in naturalistic
language corpora. The method was introduced in "ALIGN: Analyzing
Linguistic Interactions with Generalizable techNiques" (Duran, Paxton, &
Fusaroli, 2019; Psychological Methods).Installationalignmay be downloaded directly usingpip.To download the stable version released on PyPI:pip install alignTo download directly from our GitHub repo:pip install git+https://github.com/nickduran/align-linguistic-alignment.gitAdditional tools required for somealignoptionsThe Google News pre-trained word2vec vectors (GoogleNews-vectors-negative300.bin)
and the Stanford part-of-speech tagger (stanford-postagger-full-2020-11-17)
are required for some optionalalignparameters but must be downloaded
separately.Google News:https://code.google.com/archive/p/word2vec/(page) orhttps://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing(direct download)Stanford POS tagger:https://nlp.stanford.edu/software/tagger.shtml#Download(page)
orhttps://nlp.stanford.edu/software/stanford-tagger-4.2.0.zip(direct download)TutorialsWe created Jupyter Notebook tutorials to provide an easily accessible
step-by-step walkthrough on how to usealign. Below are descriptions of the
current tutorials that can be found in theexamplesdirectory within this
repository. If unfamiliar with Jupyter Notebooks, instructions for installing
and running can be found here:http://jupyter.org/install. We recommend installing
Jupyter using Anaconda. Anaconda is a widely-used Python data science platform
that helps streamline workflows. A major advantage is that Anaconda also makes it easy
to set up unique Python environments - which may be necessary to runalignand the tutorials givenalignis currently optimized for Python 3.Jupyter Notebook 1: CHILDESThis tutorial walks users through an analysis of conversations from a
single English corpus from the CHILDES database (MacWhinney,
2000)---specifically, Kuczaj’s Abe corpus (Kuczaj, 1976). We analyze the
last 20 conversations in the corpus in order to explore how ALIGN can be
used to track multi-level linguistic alignment between a parent and child
over time, which may be of interest to developmental language researchers.
Specifically, we explore how alignment between a parent and a child
changes over a brief span of developmental trajectory.Jupyter Notebook 2: Devil's AdvocateThis tutorial walks users throught the analysis reported in (Duran,
Paxton, & Fusaroli, 2019). The corpus consists of 94 written
transcripts of conversations, lasting eight minutes each, collected from
an experimental study of truthful and deceptive communication. The goal
of the study was to examine interpersonal linguistic alignment between
dyads across two conversations where participants either agreed or
disagreed with each other (as a randomly assigned between-dyads condition)
and where one of the conversations involved the truth and the other
deception (as a within-subjects condition).We are in the process of adding more tutorials and would welcome additional
tutorials by interested contributors.AttributionIf you find the package useful, please cite our manuscript:Duran, N., Paxton, A., & Fusaroli, R. (2019). ALIGN: Analyzing
Linguistic Interactions with Generalizable techNiques.Psychological Methods.http://dynamicog.org/papers/Licensing of example dataCHILDESExample corpus "Kuczaj Corpus" by Stan Kuczaj is licensed under a
Creative Commons Attribution-ShareAlike 3.0 Unported License
(https://childes.talkbank.org/access/Eng-NA/Kuczaj.html):Kuczaj, S. (1977). The acquisition of regular and irregular past tense
forms.Journal of Verbal Learning and Verbal Behavior, 16, 589–600.Devil's AdvocateThe complete de-identified dataset of raw conversational transcripts
is hosted on a secure protected-access repository provided by the
Inter-university Consortium for Political and Social Research
(ICPSR). Please click on the link to access:http://dx.doi.org/10.3886/ICPSR37124.v1.
Due to the requirements of our IRB, please note that users interested in
obtaining these data must complete a Restricted Data Use Agreement, specify
the reason for the request, and obtain IRB approval or notice of exemption for their research.Duran, Nicholas, Alexandra Paxton, and Riccardo
Fusaroli. Conversational Transcripts of Truthful and
Deceptive Speech Involving Controversial Topics,
Central California, 2012. ICPSR37124-v1. Ann Arbor,
MI: Inter-university Consortium for Political and
Social Research [distributor], 2018-08-29.