Gaurav Kamath
@grvkamath.bsky.social
PhD-ing at McGill Linguistics + Mila, working under Prof. Siva Reddy. Mostly computational linguistics, with some NLP; habitually disappointed Arsenal fan
What's most likely is that this IS a factor for a portion of our more recent data, but not enough to affect the main finding here (across a range of words and decades). Tyvm for the interest in this!!
Cool article that's relevant: www.newyorker.com/magazine/200...
Cool article that's relevant: www.newyorker.com/magazine/200...
July 30, 2025 at 2:12 AM
What's most likely is that this IS a factor for a portion of our more recent data, but not enough to affect the main finding here (across a range of words and decades). Tyvm for the interest in this!!
Cool article that's relevant: www.newyorker.com/magazine/200...
Cool article that's relevant: www.newyorker.com/magazine/200...
Very valid q! It's likely a confound for some of the more recent data, but not most. (i) lots of the "speeches" are in fact shorter replies and remarks; (ii) the professionalization of speech-writing evolved over the 20th century, but we see no change in speakers' adoption behavior over time.
July 30, 2025 at 2:12 AM
Very valid q! It's likely a confound for some of the more recent data, but not most. (i) lots of the "speeches" are in fact shorter replies and remarks; (ii) the professionalization of speech-writing evolved over the 20th century, but we see no change in speakers' adoption behavior over time.
tysm, means a lot coming from you!
July 30, 2025 at 1:41 AM
tysm, means a lot coming from you!
Ultimately, we hope the insights from this work spur more work that uses tools from NLP to answer questions about human language.
Massive thanks to co-auths: Michelle Yang, @sivareddyg.bsky.social, @msonderegger.bsky.social and @dallascard.bsky.social!
Paper: bit.ly/4fcWfma. (12/12)
Massive thanks to co-auths: Michelle Yang, @sivareddyg.bsky.social, @msonderegger.bsky.social and @dallascard.bsky.social!
Paper: bit.ly/4fcWfma. (12/12)
PNAS
Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
bit.ly
July 29, 2025 at 12:06 PM
Ultimately, we hope the insights from this work spur more work that uses tools from NLP to answer questions about human language.
Massive thanks to co-auths: Michelle Yang, @sivareddyg.bsky.social, @msonderegger.bsky.social and @dallascard.bsky.social!
Paper: bit.ly/4fcWfma. (12/12)
Massive thanks to co-auths: Michelle Yang, @sivareddyg.bsky.social, @msonderegger.bsky.social and @dallascard.bsky.social!
Paper: bit.ly/4fcWfma. (12/12)
Limitations: Congressional speech is time-annotated linguistic data, from thousands of speakers whose ages are known, across over a century—rare, required properties for this study. But Congress was and is not socially representative. Plus: what about other languages and societies? (11/12)
July 29, 2025 at 12:06 PM
Limitations: Congressional speech is time-annotated linguistic data, from thousands of speakers whose ages are known, across over a century—rare, required properties for this study. But Congress was and is not socially representative. Plus: what about other languages and societies? (11/12)
…while at a methodological level, they suggest that sociolinguists should avoid relying too much on apparent time differences—i.e. using older speakers as a window into the past—to identify ongoing semantic shifts. (10/12)
July 29, 2025 at 12:06 PM
…while at a methodological level, they suggest that sociolinguists should avoid relying too much on apparent time differences—i.e. using older speakers as a window into the past—to identify ongoing semantic shifts. (10/12)
Our findings have both conceptual and methodological implications. At a conceptual level, they suggest that the social dynamics of word meaning change are generation-agnostic, and that speakers are capable of adapting their lexicon well into adulthood (unlike, e.g., their phonology)... (9/12)
July 29, 2025 at 12:06 PM
Our findings have both conceptual and methodological implications. At a conceptual level, they suggest that the social dynamics of word meaning change are generation-agnostic, and that speakers are capable of adapting their lexicon well into adulthood (unlike, e.g., their phonology)... (9/12)
These findings extend to the level of the individual: members of Congress that gave speeches over a long enough period of time showed significant changes in how they used some of our target words, mimicking population-level trends in word meaning change. (8/12)
July 29, 2025 at 12:06 PM
These findings extend to the level of the individual: members of Congress that gave speeches over a long enough period of time showed significant changes in how they used some of our target words, mimicking population-level trends in word meaning change. (8/12)
Overall, we find that age has very little effect—older speakers lag slightly behind younger ones, but match their word usage within just a few years; in some cases, they even lead change. Semantic change appears driven almost purely by time, with only minor inter-generational differences. (7/12)
July 29, 2025 at 12:06 PM
Overall, we find that age has very little effect—older speakers lag slightly behind younger ones, but match their word usage within just a few years; in some cases, they even lead change. Semantic change appears driven almost purely by time, with only minor inter-generational differences. (7/12)
Finally, we use Generalized Additive Mixed-effect Models (GAMMs) to model the likelihood of a word being used in a specific sense, given the year of its use and a speaker’s age at the time, while accounting for other inter-speaker variation. (6/12)
July 29, 2025 at 12:06 PM
Finally, we use Generalized Additive Mixed-effect Models (GAMMs) to model the likelihood of a word being used in a specific sense, given the year of its use and a speaker’s age at the time, while accounting for other inter-speaker variation. (6/12)
We identify >100 words suspected to have undergone meaning change in our corpus. We then use a Masked Language Model to induce several distinct, interpretable senses of each of these words, by clustering the MLM’s substitution predictions for the target word given different usage contexts. (5/12)
July 29, 2025 at 12:06 PM
We identify >100 words suspected to have undergone meaning change in our corpus. We then use a Masked Language Model to induce several distinct, interpretable senses of each of these words, by clustering the MLM’s substitution predictions for the target word given different usage contexts. (5/12)
To answer these questions, we conduct the first large-scale investigation of semantic change across both time and speaker age. We look at ~7.9M speeches from the U.S. Congress from 1873-2010, to ask whether semantic changes are led only by specific generations, or if everyone joins in. (4/12)
July 29, 2025 at 12:06 PM
To answer these questions, we conduct the first large-scale investigation of semantic change across both time and speaker age. We look at ~7.9M speeches from the U.S. Congress from 1873-2010, to ask whether semantic changes are led only by specific generations, or if everyone joins in. (4/12)
Example: “workshop” used to solely refer to a physical place of work; now it refers to a type of conference/seminar. As this change occurred, did older speakers learn to use “workshop” in its newer sense? Or did the dominant meaning of the word change only because those generations died out? (3/12)
July 29, 2025 at 12:06 PM
Example: “workshop” used to solely refer to a physical place of work; now it refers to a type of conference/seminar. As this change occurred, did older speakers learn to use “workshop” in its newer sense? Or did the dominant meaning of the word change only because those generations died out? (3/12)
A major question in linguistics is how languages evolve within our lifetimes. Change could be purely down to inter-generational turnover; or it could involve people of old and new generations alike participating in ongoing changes of language use. (2/12)
July 29, 2025 at 12:06 PM
A major question in linguistics is how languages evolve within our lifetimes. Change could be purely down to inter-generational turnover; or it could involve people of old and new generations alike participating in ongoing changes of language use. (2/12)
…while at a methodological level, they suggest that sociolinguists should avoid relying too much on "apparent time" differences—i.e. using older speakers as a window into the past—to identify ongoing semantic shifts. (10/12)
July 29, 2025 at 11:58 AM
…while at a methodological level, they suggest that sociolinguists should avoid relying too much on "apparent time" differences—i.e. using older speakers as a window into the past—to identify ongoing semantic shifts. (10/12)
Our findings have both conceptual and methodological implications. At a conceptual level, they suggest that the social dynamics of word meaning change are generation-agnostic, and that speakers are capable of adapting their lexicon well into adulthood (unlike, e.g., their phonology)... (9/12)
July 29, 2025 at 11:58 AM
Our findings have both conceptual and methodological implications. At a conceptual level, they suggest that the social dynamics of word meaning change are generation-agnostic, and that speakers are capable of adapting their lexicon well into adulthood (unlike, e.g., their phonology)... (9/12)
These findings extend to the level of the individual: members of Congress that gave speeches over a long enough period of time showed significant changes in how they used some of our target words, mimicking population-level trends in word meaning change. (8/12)
July 29, 2025 at 11:58 AM
These findings extend to the level of the individual: members of Congress that gave speeches over a long enough period of time showed significant changes in how they used some of our target words, mimicking population-level trends in word meaning change. (8/12)
Overall, we find that age has very little effect—older speakers lag slightly behind younger ones, but match their word usage within just a few years; in some cases, they even lead change. Semantic change appears driven almost purely by time, with only minor inter-generational differences. (7/12)
July 29, 2025 at 11:58 AM
Overall, we find that age has very little effect—older speakers lag slightly behind younger ones, but match their word usage within just a few years; in some cases, they even lead change. Semantic change appears driven almost purely by time, with only minor inter-generational differences. (7/12)
Finally, we use Generalized Additive Mixed-effect Models (GAMMs) to model the likelihood of a word being used in a specific sense, given the year of its use and a speaker’s age at the time, while accounting for other inter-speaker variation. (6/12)
July 29, 2025 at 11:58 AM
Finally, we use Generalized Additive Mixed-effect Models (GAMMs) to model the likelihood of a word being used in a specific sense, given the year of its use and a speaker’s age at the time, while accounting for other inter-speaker variation. (6/12)
We identify >100 words suspected to have undergone meaning change in our corpus. We then use a Masked Language Model to induce several distinct, interpretable senses of each of these words, by clustering the MLM’s substitution predictions for the target word given different usage contexts. (5/12)
July 29, 2025 at 11:58 AM
We identify >100 words suspected to have undergone meaning change in our corpus. We then use a Masked Language Model to induce several distinct, interpretable senses of each of these words, by clustering the MLM’s substitution predictions for the target word given different usage contexts. (5/12)
To answer these questions, we conduct the first large-scale investigation of semantic change across both time and speaker age. We look at ~7.9M speeches from the U.S. Congress from 1873-2010, to ask whether semantic changes are led only by specific generations, or if everyone joins in. (4/12)
July 29, 2025 at 11:58 AM
To answer these questions, we conduct the first large-scale investigation of semantic change across both time and speaker age. We look at ~7.9M speeches from the U.S. Congress from 1873-2010, to ask whether semantic changes are led only by specific generations, or if everyone joins in. (4/12)