CLEF 2019

At this year’s in Lugano I presented a poster on How Lexical Gold Standards Have Effects On
The Usefulness Of Text Analysis Tools For
Digital Scholarship
, presented work on detecting signs of eating disorders in social media posts done by my student Elena Fano as her master thesis,
and, at the newly instituted industrial session I discussed thresholds for adopting systematic evaluation schemes in operational settings. This last presentation was largely based on the chapter on these sorts of things in the new CLEF book.

Gunnel Källgren-seminarium

Jag hade glädjen att få hålla seminarium på institutionen där jag studerat och doktorerat i samband med att stipendiet till Gunnel Källgrens minne delades ut. Gunnel var min handledare under större delen av doktorandstudierna men hann tyvärr lämna oss innan jag hann klart :-(. Ljusbilder

Talk at UC Davis

I was honoured to be invited to UC Davis, a short train ride from Stanford, by Raul Aranovich to give a talk on “Hyperdimensional computing for human data meets the squinting linguist” or “Explicitly encoded high-dimensional semantic spaces used for authorship profiling” at the linguistics department there! Slides are here.

Webinar for ACM

I gave my first webinar ever, for the ACM, at the invitation of Rose Paradis. The title was An encoding model for hypothesis driven research on large heterogeneous data streams (OR “The squinting linguist meets hyperdimensional computing”).

Giving a webinar was a strange experience: talking to an audience of more than a thousand people but not seeing them in the room. It will take some time to get used to this sort of thing! Slides are here and the talk itself is published by the ACM on video which is a bit strange since it is mostly audio (plus the slides, of couorse).

verbs and their occurrence frequencies

Words, concepts, and usefulness of texts

Most of the tools built for practical analysis of texts are built for topical analysis and are based on lexical statistics. This means, in its simplest form, is that if a term is observed in text, it is a notable event to some extent. How notable it is can be estimated from previous experience of how often that term has been observed. That term can then be understood to refer to some concept or topic of interest with some level of likelihood or certainty. How likely it is that the term in question refers to the concept at hand can again be estimated from previous experience. The difference is that concepts are not observable other than by virtue of linking the terms observed in the text to the effect texts have on their readers. What that effect is can be studied in various ways to see whether readers are happy or not to peruse some given text given the task or situation they are in. Much research effort has been put into formalisation and experimentation with those two sources of experience, with representing concepts or topics in various ways, and into establishing if a text has the desired effect or not.

There is more to text than topic.

But almost the entire effort in those studies has been based on topical and referential analysis of text. There is more to text than topic. There are numerous reasons why one might be interested in these other factors, but since most tools are built along the principles above and heavily optimised to make use of previous experience about word occurrence, conceptual content, and topical usefulness of texts, there are precious few tools to sort out texts by other criteria. Many tasks are only to some extent topical in nature and have largely to do with other systems of language than the immediately referential, such as tracing power relations expressed in texts, understanding whose perspective is being reported in texts, or modelling change over time in attitude towards some societal phenomenon. Topical tools are not optimised for this purpose, and indeed even systematically suppress non-topical and non-referential features of text.

Verbs carry much of this information

Attitude, tense, mood, aspect, argumentation, stance of author, audience design assumptions, implicature, are all harboured in the text, overlayed on the patterns which are used to carry the topical and referential meaning.
A central category of interest for this sort of information in the linguistic signal is the verb. Verb phrases are the backbone of utterances. They refer themselves to a process, event, or state of the world; organise the referential expressions of a clause into argument structures; carry information to indicate participants’ roles in a clause; indicate in various ways temporality, aspectuality, and modality; and have attributes for manner, likelihood, veracity, and other such characteristics of an utterance.

Verbs and their occurrence statistics are typically disregarded

For topical analyses, verbs usually wash out in the statistics: a model focussed on the specifics of conceptual content of a text, verbs are too broadly distributed to be useful. There are more nouns than verbs. Verbs occur repeatedly and less burstily than nouns. This post and some following posts are intended to demonstrate some of differences of interest and relevance between noun phrases and verb phrases.

Descriptive statistics

These following data are taken from two years of 1990s newsprint previously used for information retrieval research in various shared tasks. It consists of 170 255 documents, 5 726 822 utterances, and
72 339 348 words.

Number of observations 24 349 780 5 200 970 6 730 550
Number of different observations 210 797 24 494 7 656

The basis for many information retrieval algorithms is the notion of collection frequency or idf.1 How widely dispersed an item is across documents is a fair measure of useful it is to pick out interesting items: “and” is less useful than “very” which is less useful than “oscillator”. Verbs are more widely dispersed than other lexical classes. The below table gives some basic statistics. The first half of the table shows how many verbs, nouns, or adjectives occur in over 100, 200, 500, or 1000 of the 170 355 documents in the collection these statistics are taken from. The second half of the table shows how many verbs, nouns, or adjectives occur more than twice in that number of documents.

  100   200   500   1000  
N 5.61% 11835 3.75% 7912 2.05% 4320 1.25% 2641
V 27.91% 2137 20.08% 1537 11.89% 910 7.77% 754
A 15.00% 3674 9.48% 2322 4.79% 1174 2.84% 595
  100   200   500   1000  
N 2.02% 4249 1.28% 2697 0.63% 1337 0.36% 113
V 6.49% 497 4.44% 340 2.44% 187 1.48% 696
A 2.80% 686 1.81% 444 0.91% 223 0.52% 127

This shows that a large proportion of the relatively few verbs are dispersed very widely and consequently weighted to be less interesting by most if not all topic modelling algorithms (certainly every algorithm I ever have had reason to implement uses dispersal statistics as one of the most basic assessments of relevance of a term). However, they might well be interesting for other analyses, in spite of not being topically specific! I will return to this.

1. Sparck Jones, Karen. “A statistical interpretation of term specificity and its application in retrieval.” Journal of documentation 28, no. 1 (1972): 11-21.

CHIIR March 2018

I made the trip from Stanford to the East Coast to give a demo of Gavagai Explorer at ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR) (pronounced “cheer”) which was hosted by Rutgers in the not-so-sunny-compared-to-Stanford New Brunswick. CHIIR is a new conference and it is a pleasure to participate in the emerging society around the questions of interacting with information systems! a href=”” title=”chiir-poster”>Demo poster is here and paper, co-authored by the entire Gavagai crew is here.


Everything before “But”

I have long wondered what effect the term “but” and others like it have on sentences such as “The restaurant serves nice food but the service is awful.” In general, it would seem that that what comes after “but” trumps what comes before. Here is a little study to confirm that so is indeed the case, but that this varies a bit depending on polarity of the target sentence and on the gold standard corpus under investigation.

Information structure, topic-comment, topicalisation, and sentiment analysis

I have run some experiments over the past year to figure out what happen if we weighted attitudinal items differentially according to their position in the utterance, thinking that topicalisation and the vanilla topic-comment structure might have something to say about which items are the most interesting. Yes, they do, but how to make use of it is not entirely clear.

A brief report of those experiments is here.