vMF Sentence LDA: A Spherical Topic Model over Sentence Embeddings

Ryotaro Kobayashi; Yuri Murayama; Kiyoshi Izumi

cs.CLOct 4, 2026

vMF Sentence LDA: A Spherical Topic Model over Sentence Embeddings

Authors: Ryotaro Kobayashi, Yuri Murayama, Kiyoshi Izumi

Organizations: Graduate School of Engineering, The University of Tokyo, Tokyo, Japan

Abstract

Latent Dirichlet Allocation (LDA) and models derived from it remain widely used topic models. LDA observes each document as a bag-of-words and models each topic by a categorical distribution over the vocabulary, so that it uses neither the internal structure of the document nor the similarity in meaning between words. Earlier work has responded to this limitation in two ways: many models have introduced embeddings, and some have assigned topics to sentences rather than to words. Their combination, a topic model that observes sentence embeddings, remains little explored. We propose vMF Sentence LDA (vSLDA), which keeps the admixture structure of LDA, observes each sentence as its L2-normalized embedding and models each topic by a von Mises-Fisher (vMF) distribution, which matches the cosine geometry of sentence embeddings. Its per-topic parameter count and per-iteration cost are linear in the embedding dimension, versus quadratic for the full-covariance Gaussian distribution in the existing model over sentence embeddings. We evaluate vSLDA where the limitation is expected to matter most, among topics that share much of their vocabulary: the topics that subdivide the one subject of a collection, and the narrow topics that result when a corpus is divided into a large number of topics. On two corpora, vSLDA attains the best mean rank against eight baselines when the fine categories within each coarse category are classified from the document-topic distributions. On the whole corpus, its advantage appears or widens as the number of topics grows. Weighting the word frequencies of each sentence by its topic posterior yields expected topic-word counts of the same form as those of LDA, so that the standard topic coherence and diversity measures apply to models that assign topics to sentences. On their product, topic quality, vSLDA leads in most within-category conditions of both corpora.

Figures & tables

Panel of Figure 1 — Figure 1: Graphical model of vMF Sentence LDA (vSLDA). The plates repeat over the $N_{d}$ sentences of document $d$ , the $D$ documents and the $K$ topics. $\bm{\alpha}$ is the Dirichlet parameter, $\bm{\theta}_{d}$ the document-topic distribution of document $d$ , $z_{di}$ the topic assignment of its sentence $i$ , $\bm{w}_{di}$ the normalized sentence embedding of that sentence, and $(\bm{\mu}_{k},\kappa_{k})$ the mean direction and concentration parameter of the vMF distribution of topic $k$ .

	Observation distribution
Observation unit	Categorical over the vocabulary	Gaussian over embeddings, $O(M^{2})$	vMF over embeddings, $O(M)$
Word	LDA ( Blei et al., 2003 )	GLDA ( Das et al., 2015 )	vLDA ( Li et al., 2016 )
Sentence	SentLDA ( Balikas et al., 2016 )	GSLDA ( Cha & Lee, 2024 )	vSLDA (proposed)
Document	–	–	SAM ( Reisinger et al., 2010 )

Table 1: Compared models by observation unit (rows) and observation distribution (columns). Each cell names the model of Section 2 that we take as the representative of that combination. Other models discussed there fall in the same cells. vSLDA is the proposed model, and “–” marks a combination for which no model was discussed. The

O(\cdot)

entries in the column headers give the cost of evaluating one observation’s likelihood under one topic as a function of the embedding dimension

M

(Section 3.4 ).

Figure 2: Topic-count sweep on the whole corpus (MiniLM). Rows: 20 Newsgroups (top) and NYT (bottom); columns: the accuracy (%) of fine-category classification by logistic regression on the document-topic distributions,

C_{V}

topic coherence and topic quality, the product of

C_{V}

and topic diversity (the diversity panels are in Appendix H ). The horizontal axis is the number of topics

K

. Each point is the mean over 5 runs (

n=5

), shaded bands are

\pm 1

standard deviation, and the eight models with runs across the whole grid are compared. Solid lines are the sentence-level models (SentLDA, GSLDA, vSLDA) and dashed lines the word- and document-level models.

(a)
X11/Unix windowing	UI events/widgets
( $\kappa=267.3$ )	( $\kappa=140.2$ )
( $H=0.17$ )	( $H=0.91$ )
xterm	button
xdm	expose
xmu	ctrl

Table 3: Groups of vSLDA topics matched to one SentLDA topic (

K=20

, MiniLM, run 0; every topic of both runs is listed in Appendix Q ). In each of the two runs, every vSLDA topic is matched to the SentLDA topic that receives the largest expected co-assignment of its sentences (not necessarily a majority of them; Appendix Q ); each panel shows the largest group of vSLDA topics matched to one SentLDA topic whose dominant fine category holds at least

50

% of its posterior mass. (a) 20 Newsgroups/ computer : two topics matched to the SentLDA topic with the representative words cursor , icon , event , ctrl , expose ; (b) NYT/ arts : three topics matched to the SentLDA topic with the representative words orchestra , philharmonic , carnegie , symphony , gilbert . Each column gives the fitted concentration parameter

\kappa

, the normalized fine-category entropy

H

of the topic’s posterior sentence mass and the top 5 representative words; within a panel the topics are ordered by ascending

H

. The italic label above each topic is a descriptive label assigned by the authors from its representative words and is not an output of the model.

Appendix figures & tables28 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Coarse category	Fine categories
20news	computer	comp.{graphics, os.ms-windows.misc, sys.ibm.pc.hardware, sys.mac.hardware, windows.x}
	ride	rec.{autos, motorcycles}
	sports	rec.sport.{baseball, hockey}
	science	sci.{crypt, electronics, med, space}
	religion	alt.atheism, soc.religion.christian, talk.religion.misc
	politics	talk.politics.{guns, mideast, misc}

Appendix

Table A.1: Coarse categories (experimental units) of 20 Newsgroups (20news) and NYT, and their fine categories. For 20 Newsgroups, braces abbreviate a shared prefix (e.g., rec.{autos, motorcycles} denotes rec.autos and rec.motorcycles).

Dataset	Coarse cat.	# Fine cat.	# Docs	# Sentences	Sent./doc	Tokens/sent
20news	computer	5	4,727	39,881	8.44 $\pm$ 27.61	15.88 $\pm$ 11.35
	ride	2	1,875	12,039	6.42 $\pm$ 9.78	15.84 $\pm$ 10.87
	sports	2	1,868	14,300	7.66 $\pm$ 14.41	15.85 $\pm$ 11.36
	science	4	3,807	37,181	9.77 $\pm$ 23.94	17.44 $\pm$ 11.89
	religion	3	2,344	27,963	11.93 $\pm$ 18.46	17.76 $\pm$ 12.46
	politics	3	2,532	33,089	13.07 $\pm$ 27.68	17.91 $\pm$ 13.52

Appendix

Table A.2: Statistics of the experimental units (coarse categories) of 20 Newsgroups (20news) and NYT. “# Docs” combines the training and test sets, “Sent./doc” is the mean number of sentences per document (

\pm

standard deviation), and “Tokens/sent” is the mean number of tokens per sentence (

\pm

standard deviation), counted over the evaluation vocabulary.

20 Newsgroups				NYT
Unit	$\|F\|$	JSD	$\cos$	Unit	$\|F\|$	JSD	$\cos$
Computer	5	0.164	0.781	Arts	4	0.224	0.760
Ride	2	0.129	0.838	Business	4	0.213	0.693
Sports	2	0.169	0.821	Politics	9	0.230	0.602
Science	4	0.203	0.512	Sports	7	0.231	0.695
Religion	3	0.105	0.935

Appendix

Table B.1: Distance between the fine categories inside each experimental unit.

|F|

is the number of fine categories of the unit. JSD is the mean Jensen–Shannon divergence (base 2) between their unigram distributions and

\cos

the mean cosine similarity between their centroid directions, both averaged over the pairs of the unit; a larger JSD and a smaller

\cos

indicate fine categories that lie further apart. JSD and

\cos

are averaged over the 200 equal-sized draws described in the text; their standard deviation over the draws is at most 0.001 and 0.007. The Different units row gives the same pair averages over fine-category pairs drawn from different units. The sentence encoder is MiniLM.

Item	Setting
Common to all models
Runs and seeds	5 independent training runs of the topic model per condition; run $i$ trains with seed $42+i$ , set jointly for the Python, NumPy and PyTorch generators; classifier random state fixed across runs
Topic counts	$K\in\{10,20,30\}$ (main experiments); $K\in\{10,20,30,50,100,200,300\}$ on the whole corpus and up to $K=100$ within the coarse categories (Section 5.3 ; GLDA only within the coarse categories at $K\leq 30$ , and GSLDA within the coarse categories only at $K\leq 30$ )
Sentence encoders	all-MiniLM-L6-v2 ( $M=384$ ; main text); all-mpnet-base-v2 and bge-base-en-v1.5 ( $M=768$ ; Appendix K ); outputs L2-normalized for vSLDA and GSLDA (except the refits of Appendix N ), unnormalized for ConTM; no instruction prefix for BGE
Word embeddings	word2vec-google-news-300 ( $M=300$ ) for GLDA, vLDA and ETM, whose vocabularies are the word types with a pretrained vector; L2-normalized for vLDA
Preprocessing	sentence segmentation with pysbd ; rule-based sentence filter (sentences of fewer than 4 or more than 120 word tokens and fragments without sentence structure dropped; URLs, e-mail addresses and non-ASCII characters removed); tokenization, lowercasing, WordNet lemmatization, numerals mapped to <NUM> , single-character tokens dropped; no stopword or part-of-speech filtering; sentences truncated at the encoder’s default maximum length (256/384/512 word pieces)

Appendix

Table D.1: Hyperparameters, preprocessing settings and random seeds of the experiments (Section 4.4 ).

K

is the number of topics,

M

the embedding dimension and

i\in\{0,\dots,4\}

the run index.

Model	Encoder ( $M$ )	Per sweep (s)	Sweeps	Training (s)
vSLDA	MiniLM (384)	0.038 $\pm$ 0.000	200	7.74 $\pm$ 0.03
vSLDA	MPNet (768)	0.055 $\pm$ 0.000	200	11.0 $\pm$ 0.1
GSLDA	MiniLM (384)	60.2 $\pm$ 11.2	10	622.7 $\pm$ 82.9
GSLDA	MPNet (768)	288.0 $\pm$ 0.8	10	2,880 $\pm$ 8

Appendix

Table E.1: Wall-clock training time of vSLDA and GSLDA on the computer unit of 20 Newsgroups (

K=20

; mean

\pm

standard deviation over 3 runs). Per sweep is the time of one Gibbs sweep over the training sentences, averaged over all recorded sweeps and including the per-iteration work around the sweeps. Training is the time of the training length of the main experiments, 10 outer iterations (200 sweeps) for vSLDA and 10 Gibbs sweeps for GSLDA. The setting and what each column includes are described in the text.

Figure E.1: Sampler convergence against wall-clock time (20 Newsgroups/ computer ,

K=20

, MiniLM). Each panel shows the average log-likelihood of the training sentences against the cumulative training time in seconds, on a logarithmic scale, for vSLDA (left) and GSLDA (right), with one line per run (3 runs). The vMF likelihood is a density on the unit hypersphere and the GSLDA likelihood a Student-

t

posterior predictive density on

\mathbb{R}^{M}

, so the two are not comparable in value. Each panel therefore carries its own vertical axis and only the horizontal axis is shared. The dotted rule marks the training length of the main experiments, 10 outer iterations for vSLDA and 10 Gibbs sweeps for GSLDA. Both samplers reach their plateau within that length.

Figure G.1: Sample efficiency per category on 20 Newsgroups (MiniLM). Rows are the coarse categories computer , ride and sports (the other three follow on the next page) and columns the topic counts

K=10

20

and

30

. The vertical axis is the accuracy (%) of fine-category classification by logistic regression on the document-topic distributions, and the horizontal axis is the fraction (%) of training documents whose labels were used to train the classifier. The three panels of a row share the vertical range. Each point is the mean over 5 runs and, below 100%, 5 labeled subsets drawn per run (

n=25

;

n=5

at 100%), shaded bands are

\pm 1

standard deviation over the same values (Section 4.3 ), and the nine models of the main experiments are compared. Solid lines are the sentence-level models (SentLDA, GSLDA, vSLDA) and dashed lines the word- and document-level models.

Figure G.2: Sample efficiency per category on 20 Newsgroups (MiniLM), continued. Rows are the coarse categories science , religion and politics and columns the topic counts

K=10

20

and

30

; axes, points, bands and line styles as on the previous page.

Figure G.3: Sample efficiency per category on NYT (MiniLM). Rows are the four coarse categories and columns the topic counts

K=10

20

and

30

; axes, points, bands and line styles as in Figure G.1 .

Figure H.1: Topic diversity in the topic-count sweep on the whole corpus (MiniLM). Left: 20 Newsgroups; right: NYT. The vertical axis is topic diversity, the horizontal axis the number of topics

K

; accuracy,

C_{V}

coherence and topic quality on the same sweep are in Figure 2 . Each point is the mean over 5 runs (

n=5

), shaded bands are

\pm 1

Figure H.2: Topic-count sweep of classification accuracy within the coarse categories of 20 Newsgroups (MiniLM). Each panel is one coarse category. The vertical axis is the accuracy (%) of fine-category classification by logistic regression on the document-topic distributions, and the horizontal axis is the number of topics

K

(the per-category runs stop at

K=100

). Each point is the mean over 5 runs (

n=5

), shaded bands are

\pm 1

standard deviation, and the seven models with runs across the grid are compared. Solid lines are the sentence-level models (SentLDA, vSLDA) and dashed lines the word- and document-level models.

Figure H.3: Topic-count sweep of classification accuracy within the coarse categories of NYT (MiniLM). Each panel is one coarse category. The vertical axis is the accuracy (%) of fine-category classification by logistic regression on the document-topic distributions, and the horizontal axis is the number of topics

K

(the per-category runs stop at

K=100

). Each point is the mean over 5 runs (

n=5

), shaded bands are

\pm 1

standard deviation, and the seven models with runs across the grid are compared. Solid lines are the sentence-level models (SentLDA, vSLDA) and dashed lines the word- and document-level models.

Figure H.4: Topic-count sweep of topic quality within the coarse categories of 20 Newsgroups (MiniLM). Each panel is one coarse category. The vertical axis is topic quality, the product of

C_{V}

coherence and topic diversity (as in Table F.4 ), and the horizontal axis is the number of topics

K

(the per-category runs stop at

K=100

). Each point is the mean over 5 runs (

n=5

), shaded bands are

\pm 1

standard deviation, and the seven models with runs across the grid are compared. Solid lines are the sentence-level models (SentLDA, vSLDA) and dashed lines the word- and document-level models.

Figure H.5: Topic-count sweep of topic quality within the coarse categories of NYT (MiniLM). Each panel is one coarse category. The vertical axis is topic quality, the product of

C_{V}

coherence and topic diversity (as in Table F.4 ), and the horizontal axis is the number of topics

K

(the per-category runs stop at

K=100

). Each point is the mean over 5 runs (

n=5

), shaded bands are

\pm 1

standard deviation, and the seven models with runs across the grid are compared. Solid lines are the sentence-level models (SentLDA, vSLDA) and dashed lines the word- and document-level models.

Setting	20 Newsgroups				NYT
Setting	Acc. (%)	$C_{V}$	Div.	Empty	Acc. (%)	$C_{V}$	Div.	Empty
Fallback concentration $\kappa_{0}$
$1$	73.50	0.456	0.996	0.0	86.35	0.553	0.995	0.0
$10$ (main)	73.51	0.457	0.996	0.0	86.27	0.554	0.995	0.0
$100$	73.51	0.457	0.996	0.0	86.33	0.554	0.995	0.0
$1000$	73.51	0.457	0.996	0.0	86.33	0.554	0.995	0.0

Appendix

Table L.1: Sensitivity of vSLDA to its own hyperparameters. Classification accuracy (%, logistic regression),

C_{V}

topic coherence, topic diversity and number of empty topics out of

K=20

as one hyperparameter at a time is varied from the setting of the main experiments (

\kappa_{0}=10

\alpha_{0}=50/K=2.5

\zeta=20

B=8

T=10

T_{0}=5

a=1

; the row marked “main” in each block), at

K=20

with MiniLM embeddings.

\kappa_{0}

is the concentration assigned to a topic holding at most one sentence,

\alpha_{0}

the initial Dirichlet parameter,

\zeta

the number of Gibbs sweeps per E-step,

B

the number of stored sweeps,

T

the number of outer iterations,

T_{0}

the burn-in after which the statistics are averaged and

\bm{\alpha}

is held fixed (

T_{0}=T

: no averaging), and

a

the exponent of the step sizes

\gamma_{t}=(t-T_{0})^{-a}

. Values are means over the coarse-category units of each dataset and 3 runs per condition; the main setting is read from the main runs with the same seeds and therefore differs slightly from Table F.1 . An empty topic is one to which no sentence is assigned at the last Gibbs sweep of training.

Figure M.1: Per-topic and per-document distributions of the structural diagnostics (

K=20

, MiniLM). Rows, from top: the normalized document entropy

H(P(d\mid k))/\ln D

of each topic (“topic spread over documents”), the rank-1 document fraction of each topic (“share as main topic”; the axis is linear below

0.01

and logarithmic above), and the normalized entropy

H(\bm{\theta}_{d})/\ln K

of each document (“document spread over topics”); columns: 20 Newsgroups (left) and NYT (right). The models are named on the horizontal axis. Each box pools the topics (or documents) of all coarse categories of the dataset and all 5 runs: boxes span the quartiles, whiskers extend to 1.5 interquartile ranges and points mark topics beyond the whiskers (not drawn in the bottom row). Dashed lines mark the cut-offs of Table M.1 ,

0.95

for diffuse and

0.01

for dead topics, and the dotted line the equal-use value

1/K

Model	Diffuse topics (%, $\downarrow$ )				Dead topics (%, $\downarrow$ )
	$H(P(d\mid k))/\ln D>$				rank-1 document fraction $<$
	$0.85$	$0.90$	$0.95$ (main)	$0.99$	$0.001$	$0.01$ (main)	$0.02$	$0.05$
LDA (Blei, 2003)	18.4	10.6	4.1	0.1	44.6	60.4	64.8	73.7
SentLDA (Balikas, 2016)	6.1	1.9	0.0	0.0	5.0	23.0	35.5	57.9
SAM (Reisinger, 2010)	99.4	82.7	14.8	5.6	7.6	17.9	27.7	54.7
GLDA (Das, 2015)	99.3	84.7	55.0	13.8	76.2	90.0	91.1	93.9

Appendix

Table M.2: Cut-off sensitivity of panels (a) and (b) of Table M.1 : the percentage of the

K

topics of a run that are diffuse (normalized document entropy

H(P(d\mid k))/\ln D

above the cut-off) and dead (rank-1 document fraction below the cut-off), computed per run and averaged with equal weight over the 150 runs of the main experiments (the 10 categories of the two datasets

\times

K\in\{10,20,30\}

\times

5 runs; MiniLM). The columns marked (main) are the cut-offs of Table M.1 . A lower value is preferable (

\downarrow

) and bold indicates the lowest value within each column.

Figure N.1: Accuracy gap between vSLDA and GSLDA against the observations per dimension of GSLDA. Each point is one GSLDA training run (both datasets, every coarse category,

K\in\{10,20,30\}

, 5 runs, three sentence encoders;

450

points,

150

per encoder). The vertical axis is the classification accuracy of vSLDA minus that of GSLDA in the same condition (points), and the horizontal axis, on a logarithmic scale, is

n/M

, the median number of sentences assigned to a non-empty topic at the last Gibbs sweep divided by the embedding dimension

M

(

384

for MiniLM,

768

for MPNet and BGE). The dotted vertical line marks

n/M=1

, below which the sample covariance of a typical topic is singular.

		Accuracy (%)			Topic assignments of GSLDA
Dataset	$K$	GSLDA	vSLDA	$\Delta$	$n/M$	Largest (%)	Empty
20 Newsgroups	10	$35.8\pm 2.6$	$37.6\pm 2.6$	$-1.8$	26.4	16.9	0
	20	$41.6\pm 1.0$	$48.6\pm 1.0$	$-7.0$	13.4	13.8	0
	30	$42.3\pm 2.0$	$51.9\pm 1.4$	$-9.6$	3.3	13.7	0
NYT	10	$53.0\pm 3.2$	$66.6\pm 3.2$	$-13.6$	41.1	16.1	0
	20	$64.3\pm 2.4$	$77.2\pm 1.4$	$-13.0$	19.9	13.6	0

Appendix

Table N.1: GSLDA on each corpus as a whole (MiniLM). Accuracy is that of fine-category classification by logistic regression on the document-topic distributions (%, mean

\pm

standard deviation over 5 runs), and

\Delta

the mean accuracy of GSLDA minus that of vSLDA .

n/M

is the median number of sentences assigned to a non-empty topic at the last Gibbs sweep of training divided by the embedding dimension

M=384

, Largest the percentage of the sentences assigned to the largest topic and Empty the largest number of topics without a sentence in any run. The unit and the vSLDA runs are those of Figure 2 .

$\lambda$	20 Newsgroups				NYT
$\lambda$	Acc. (%)	Empty	Div.	$C_{V}$	Acc. (%)	Empty	Div.	$C_{V}$
$0.01$	49.49	0.0	0.957	0.226	52.51	1.2	0.855	0.336
$0.1$ (main)	54.95	0.0	0.969	0.249	58.02	2.1	0.810	0.342
$1$	39.90	18.2	0.125	0.273	41.31	17.2	0.181	0.289
$3$	37.34	19.0	0.050	0.258	39.04	18.8	0.075	0.269
$10$	37.34	19.0	–	–	38.72	19.0	–	–

Appendix

Table N.2: Sensitivity of GSLDA to the scale matrix of its covariance prior. Classification accuracy (%, logistic regression), number of empty topics out of

K=20

, topic diversity and

C_{V}

of GSLDA as the prior scale

\lambda

, which sets the scale matrix of the inverse-Wishart prior on the topic covariances to

\lambda\bm{I}

, is varied at

K=20

with MiniLM embeddings.

\lambda=0.1

is the setting of the main experiments,

\lambda=3

the value reported for Gaussian LDA, and

\lambda=3M=1152

the default of the reference implementation. Values are means over the coarse-category units of each dataset and 3 runs per condition. An empty topic is one to which no sentence is assigned at the last Gibbs sweep of training; from

\lambda=10

upwards

19

of the

20

topics are empty in every run, so the word-based measures cannot be computed (–). The last two rows give the same four quantities for vSLDA and SentLDA over the same runs, whose accuracy, diversity and

C_{V}

therefore differ slightly from Tables F.1 and F.2 . A higher value is preferable in every column except Empty, and bold marks the highest accuracy of GSLDA on each dataset.

Model	Classification accuracy		Topic assignments
Model	$\Delta$ vs. vSLDA	Ahead (of 30)	Empty (%)	Largest (%)
GSLDA, full	$-22.4$	0	4.0	51.0
GSLDA, diagonal	$-0.8$	9	30.2	16.2
GSLDA, isotropic	$0.0$	10	0.0	11.1
Unnormalized input
Full	$-30.1$	0	0.0	63.3

Appendix

Table N.3: Classification accuracy relative to vSLDA and topic-assignment diagnostics of the sentence-level Gaussian topic model under the three covariance structures (MiniLM). Full is the normal–inverse-Wishart model of Table F.1 ; diagonal and isotropic replace it by scaled-inverse-

\chi^{2}

priors on the variances, leaving the prior on the mean, the prior width of an empty topic and every other setting unchanged. The rows under unnormalized input repeat the three structures on the pooled encoder output taken before the terminal normalization module of the released encoder.

\Delta

is the classification accuracy of the structure (%, logistic regression) minus that of vSLDA , averaged over the 30 conditions of Table F.1 (5 runs each), on which vSLDA averages 77.5%; Ahead is the number of those conditions in which the structure’s mean accuracy exceeds that of vSLDA . Empty is the percentage of the

K

topics to which no sentence is assigned at the last Gibbs sweep of training, and Largest the percentage of the sentences assigned to the largest topic, both averaged over the same 150 runs. The word-based measures of the refits at

K=20

are reported in the text.

Figure O.1: Expected log-density deficit of a topic against its number of observations. Each curve is the deficit

b(n)

of Eq. ( O.3 ) for one observation model, for vMF observations at

M=384

and

\kappa=250

(both axes logarithmic). The models are the full-covariance predictive of GSLDA at three prior scales

\lambda

, its diagonal and isotropic variants at

\lambda=0.1

(all with

\tau_{0}=0.1

), and the vMF likelihood of vSLDA at the estimates of its M-step. Each point is the mean over

50

independent sets of

n

observations and

2{,}000

test draws, and two Monte Carlo standard errors are at most

5

% of each value. The dotted lines are

M/(2n)

and

M(M+3)/(4n)

, the large-sample deficits of correctly specified models with the numbers of parameters of the vMF and the full-covariance topic, and the dotted vertical line marks

n=M

. The vMF and isotropic curves follow

M/(2n)

and overlap, whereas under

\lambda=0.1

and

\lambda=0.01

the full-covariance deficit peaks near

n=M

, where both are below one nat.

Model	Scale matrix	Accuracy	Largest (%)	Empty
Released implementation, as released	diagonal, Eq. ( P.1 )	$89.1\pm 0.9$	$6.2\pm 0.1$	$0.0\pm 0.0$
Released implementation, full forward substitution	full	$56.1\pm 1.2$	$80.2\pm 0.1$	$0.0\pm 0.0$
GSLDA (this paper)	full	$53.1\pm 0.8$	$92.8\pm 0.5$	$0.0\pm 0.0$
vSLDA	–	$89.4\pm 0.7$	$9.1\pm 0.4$	$0.0\pm 0.0$

Appendix

Table P.1: Classification accuracy and topic-assignment diagnostics of the released SentenceLDA implementation, GSLDA and vSLDA on 20 Newsgroups/ sports at

K=20

(all-mpnet-base-v2). The first two rows are the implementation released with Cha & Lee (2024) , run as released, where the posterior predictive has the diagonal scale matrix of Eq. ( P.1 ), and run with the forward substitution carried out in full and nothing else changed. The last two rows are the GSLDA of this paper, which evaluates the full quadratic form, and vSLDA , which has no scale matrix. Accuracy is the classification accuracy (%, logistic regression), Largest the percentage of the training sentences assigned to the largest topic and Empty the number of topics to which no sentence is assigned. Values are the mean

\pm

standard deviation over 5 runs.

Dataset	$K$	$\rho(\kappa_{k},H)$	$\rho<0$	$\rho(\kappa_{k},H\mid\log n_{k})$	$\rho<0$
20news	10	$-0.69$ [ $-0.95$ , $-0.21$ ]	30/30	$-0.60$ [ $-0.93$ , $0.12$ ]	28/30
	20	$-0.65$ [ $-0.91$ , $-0.27$ ]	30/30	$-0.63$ [ $-0.88$ , $-0.33$ ]	30/30
	30	$-0.66$ [ $-0.88$ , $-0.32$ ]	30/30	$-0.65$ [ $-0.87$ , $-0.33$ ]	30/30
NYT	10	$-0.65$ [ $-0.90$ , $-0.16$ ]	20/20	$-0.57$ [ $-0.88$ , $0.06$ ]	18/20
	20	$-0.61$ [ $-0.86$ , $-0.16$ ]	20/20	$-0.59$ [ $-0.85$ , $-0.16$ ]	20/20
	30	$-0.60$ [ $-0.82$ , $-0.24$ ]	20/20	$-0.58$ [ $-0.84$ , $-0.15$ ]	20/20

Appendix

Table Q.2: Rank correlation, within a run, between the concentration of a topic and the breadth of its fine-category distribution. For every vSLDA run of the main experiments (MiniLM; 6 units of 20 Newsgroups and 4 of NYT, 5 runs each), the Spearman correlation across the

K

topics between the fitted

\kappa_{k}

and the normalized fine-category entropy

H

of the topic’s posterior sentence mass, and the same correlation with the ranks of

\log n_{k}

(the topic size) partialled out of both. Cells give the mean over the units and runs of the row with the range in brackets, and the number of runs in which the correlation is negative out of all runs of the row. Topics to which no sentence is assigned are excluded.

Topic	$\kappa_{k}$	$n_{k}$	$H$	Fine category	Representative words
0	251.7	1254	0.87	os.ms-windows.misc (44%)	nt, msw, microsoft, window, apps
1	288.8	683	0.99	graphics (23%)	appreciate, thank, help, greatly, advance
2	138.7	1413	0.85	graphics (47%)	mahan, tgv, patrick, washer, printf
3	289.3	866	0.95	graphics (33%)	monitor, vga, resolution, hz, multisync
4	263.0	1385	0.43	graphics (80%)	graphic, visualization, ray, animation, plot
5	281.7	524	0.39	sys.mac.hardware (85%)	apple, mac, macintosh, appletalk, macweek

Appendix

Table Q.3: Every topic of vSLDA in the 20 Newsgroups/ computer run (

K=20

, MiniLM, run 0), with the fitted concentration parameter

\kappa_{k}

, its posterior sentence mass

n_{k}

, its normalized fine-category entropy

H

, its dominant fine category with the share and its top 5 representative words; the columns are defined in the text of this appendix.

Topic	$n_{k}$	$H$	Fine category	Representative words
0	1329	0.89	graphics (40%)	mail, subscribe, send, newsgroups, subscription
1	898	0.46	graphics (79%)	color, quantization, palette, quantize, lossless
2	2629	0.65	windows.x (63%)	cursor, icon, event, ctrl, expose
3	862	0.79	os.ms-windows.misc (45%)	irq, slip, xxxx, modem, dtr
4	1462	0.48	graphics (78%)	phigs, analysis, plot, conference, dimensional
5	874	0.55	graphics (74%)	format, hsi, jfif, pict, ppm

Appendix

Table Q.4: Every topic of SentLDA in the 20 Newsgroups/ computer run (

K=20

, MiniLM, run 0), with its posterior sentence mass

n_{k}

, its normalized fine-category entropy

H

, its dominant fine category with the share and its top 5 representative words; the columns are defined in the text of this appendix.

Topic	$\kappa_{k}$	$n_{k}$	$H$	Fine category	Representative words
0	258.7	365	0.53	movies (77%)	oscar, award, nominee, nomination, academy
1	164.6	802	0.90	music (39%)	cambridge, honoree, mandela, golf, roosevelt
2	262.1	949	0.31	movies (90%)	film, movie, cinema, cinemascope, ridley
3	239.1	851	0.40	television (86%)	episode, netflix, show, abc, network
4	266.8	550	0.35	music (88%)	clarinet, instrument, accordion, piano, trumpet
5	232.8	567	0.58	movies (72%)	killing, indonesia, trayvon, jfk, aftermath

Appendix

Table Q.5: Every topic of vSLDA in the NYT/ arts run (

K=20

, MiniLM, run 0), with the fitted concentration parameter

\kappa_{k}

, its posterior sentence mass

n_{k}

, its normalized fine-category entropy

H

, its dominant fine category with the share and its top 5 representative words; the columns are defined in the text of this appendix.

Topic	$n_{k}$	$H$	Fine category	Representative words
0	1175	0.57	television (67%)	malick, episode, fx, clyde, pope
1	971	0.83	music (45%)	copyright, stream, netflix, beatles, bootleg
2	143	0.51	music (79%)	ferrando, mezzo, lukas, falstaff, soprano
3	24	0.55	movies (67%)	philippe, banter, exploitation, puckish, blanchett
4	577	0.54	music (77%)	euro, subsidy, gelb, bankruptcy, endowment
5	3372	0.93	music (32%)	you, lorne, me, didn, my

Appendix

Table Q.6: Every topic of SentLDA in the NYT/ arts run (

K=20

, MiniLM, run 0), with its posterior sentence mass

n_{k}

, its normalized fine-category entropy

H

, its dominant fine category with the share and its top 5 representative words; the columns are defined in the text of this appendix.

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Traditional topic modeling assigns a single topic to each document. In practice, however, many real-world documents, such as product reviews or open-ended survey responses, contain multiple distinct topics. This mismatch often leads to topic contamination, where unrelated themes are merged into a single topic, making it difficult to identify documents that truly focus on a specific subject. We address this issue by introducing segment-based topic allocation (SBTA), a reformulation of topic modeling that assigns topics not to entire documents, but to segments: short, coherent spans of text that each express a single theme. By modeling topical structure at the segment level, our approach yields cleaner and more interpretable topics and better supports analysis of multi-theme documents. To support systematic evaluation, we construct a SemEval-STM, a new dataset inspired by aspect-based sentiment analysis. Documents are first decomposed into topical segments using large language models (LLMs), followed by human refinement to ensure segment quality. We also propose a segment-level extension of the word intrusion task, enabling human evaluation of topical coherence at the granularity where topics are actually assigned. Across multiple models and evaluation metrics, we show that SBTA improves clustering quality and interpretability. Overall, this work provides a practical, scalable framework for fine-grained topic analysis in heterogeneous text corpora where documents naturally span multiple topics. URL: https://huggingface.co/datasets/LG-AI-Research/SemEval-STM

Abstract

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