SlowGuess/NAACL_2022_OCR huggingface dataset:
Compared Methods. We compare our SEETOPIC framework with the following methods, including seed-guided topic modeling methods, seed-guided embedding learning methods, and PLMs. (1) SeededLDA (Jagarlamudi et al., 2012) $^{8}$  is a seed-guided topic modeling method. It improves LDA by biasing topics to produce input seeds and by biasing documents to select topics relevant to the seeds they contain. (2) Anchored CorEx (Gallagher et al., 2017) $^{9}$  is a seed-guided topic modeling method. It incorporates user-provided seeds by balancing between compressing the input corpus and preserving seed-related information. (3) Labeled ETM (Dieng et al., 2020) $^{10}$  is an embedding-based topic modeling method. It incorporates distributed representation of each term. Following (Meng et al., 2020a), we retrieve representative terms according to their embedding similarity with the category name. (4) CatE (Meng et al., 2020a) $^{11}$  is a seed-guided embedding learning method for discriminative topic discovery. It takes category names as input and jointly learns term embedding and specificity from the input corpus. Category-discriminative terms are then selected based on both embedding similarity with the category and specificity. (5) BERT (Devlin et al., 2019) $^{12}$  is a PLM. Following Lines 1-7 in Algorithm 1, we use BERT to encode each input category name and each term to a vector, and then perform similarity search to directly find all repre
8 https://github.com/vi3k6i5/GuidedLDA
9 https://github.com/gregversteeg/corex-topic
10https://github.com/adjidieng/ETM
11https://github.com/yumeng5/CatE
12https://huggingface.co/bert-base-uncased
4 https://github.com/allenai/scidocs
5http://jmcauley.ucsd.edu/data/amazon/index.html
$^{6}$ https://github.com/franticnerd/geoburst
7https://github.com/shangjingbo1226/AutoPhrase
Table 3: NPMI, LCP, MACC, and Diversity of compared algorithms on three datasets. NPMI and LCP measure topic coherence; MACC measures term accuracy; Diversity (abbreviated to Div.) measures topic diversity. Bold: the highest score. Underline: the second highest score. \*: significantly worse than SEETOPIC (p-value  $< 0.05$ ). \*\*: significantly worse than SEETOPIC (p-value  $< 0.01$ ).
Methods	SciDocs	Amazon	Twitter
NPMI	LCP	MACC	Div.	NPMI	LCP	MACC	Div.	NPMI	LCP	MACC	Div.
SeededLDA	0.111**	-1.232	0.156**	0.451**	0.140**	-1.505	0.147**	0.393**	0.026**	-4.508**	0.195**	0.696**
Anchored CorEx	0.213**	-2.180**	0.264**	1.000	0.268**	-1.963*	0.333**	1.000	0.180**	-4.384**	0.233**	1.000
Labeled ETM	0.669*	-1.549**	0.458**	0.961*	0.616**	-2.103**	0.585**	1.000	0.610*	-2.197**	0.268**	0.989
CatE	0.691*	-1.451**	0.633**	1.000	0.633**	-1.688**	0.856*	1.000	0.711	-1.653	0.483**	1.000
BERT	0.626**	-1.682**	0.740**	0.891**	0.588**	-2.186**	0.832**	1.000	0.626**	-2.088**	0.627	0.944**
BioBERT	0.618**	-1.704**	0.938	0.982**	-	-	-	-	-	-	-	-
SEETOPIC-NoIter	0.681**	-1.535**	0.887	1.000	0.643**	-1.971**	0.892	1.000	0.636	-2.008**	0.618	1.000
SEETOPIC	0.717	-1.267	0.909	1.000	0.684	-1.391	0.904	1.000	0.640	-1.813	0.633	1.000
sentative terms. (6) BioBERT (Lee et al., 2020) $^{13}$  is a PLM. It is used in the same way as BERT. Since BioBERT is specifically trained for biomedical text mining tasks, we report its performance on the SciDocs dataset only. (7) SEETopic-NoIter is a variant of our SEETopic framework. In Algorithm 1, after initialization (Lines 1-7), it executes Lines 9-16 only once (i.e.,  $T = 1$ ) to find all representative terms.
Here, all seed-guided topic modeling and embedding baselines (i.e., SeededLDA, Anchored CorEx, CatE, and Labeled ETM) can only take in-vocabulary seeds as input. For a fair comparison, we run Lines 1-7 in Algorithm 1 to get the initial representative in-vocabulary terms for each category, and input these terms as seeds into the baselines. In other words, all compared methods use BERT/BioBERT to initialize their term sets.
Evaluation Metrics. We evaluate topic discovery results from three different angles: topic coherence, term accuracy, and topic diversity.
(1) NPMI (Lau et al., 2014) is a standard metric in topic modeling to measure topic coherence. Within each topic, it calculates the normalized pointwise mutual information for each pair of terms in  $S_{i}$ .
$$
\mathrm{NPMI} = \frac{1}{|\mathcal{C}|}\sum_{i = 1}^{| \mathcal{C}|}\frac{1}{\binom{| \mathcal{S}_{i}|}{2}}\sum_{\substack{w_{j},w_{k}\in \mathcal{S}_{i}\\ j <   k}}\frac{\log\frac{P(w_{j},w_{k})}{P(w_{j})P(w_{k})}}{-\log P(w_{j},w_{k})}, \tag{8}
$$
where  $P(w_{j},w_{k})$  is the probability that  $w_{j}$  and  $w_{k}$  co-occur in a document;  $P(w_{j})$  is the marginal probability of  $w_{j}$ .<sup>14</sup>
(2) LCP (Mimno et al., 2011) is another standard metric to measure topic coherence. It calculates the pairwise log conditional probability of top-ranked
terms.
$$
\mathrm{LCP} = \frac{1}{|\mathcal{C}|}\sum_{i = 1}^{|c|}\frac{1}{\binom{|S_{i}|}{2}}\sum_{\substack{w_{j},w_{k}\in \mathcal{S}_{i}\\ j <   k}}\log \frac{P(w_{j},w_{k})}{P(w_{j})}. \tag{9}
$$
Note that PMI (Newman et al., 2010) is also a standard metric for topic coherence. We do observe that SEETOPIC outperforms baselines in terms of PMI in most cases. However, since our local embedding step is implicitly optimizing a PMI-like objective, we no longer use it as our evaluation metric.
(3) MACC (Meng et al., 2020a) measures term accuracy. It is defined as the proportion of retrieved terms that actually belong to the corresponding category according to the category name.
$$
\operatorname {M A C C} = \frac {1}{| \mathcal {C} |} \sum_ {i = 1} ^ {| \mathcal {C} |} \frac {1}{| \mathcal {S} _ {i} |} \sum_ {w _ {j} \in \mathcal {S} _ {i}} \mathbf {1} \left(w _ {j} \in c _ {i}\right), \tag {10}
$$
where  $\mathbf{1}(w_{j} \in c_{i})$  is the indicator function of whether  $w_{j}$  is relevant to category  $c_{i}$ . MACC requires human evaluation, so we invite five annotators to perform independent annotation. The reported MACC score is the average MACC of the five annotators. A high inter-annotator agreement is observed, with Fleiss' kappa (Fleiss, 1971) being 0.856, 0.844, and 0.771 on SciDocs, Amazon, and Twitter, respectively.
(4) Diversity (Dieng et al., 2020) measures the mutual exclusivity of discovered topics. It is the percentage of unique terms in all topics, which corresponds to our task requirement that each retrieved term is discriminatively close to one category and far from the others.
$$
\text {D i v e r s i t y} = \frac {\left| \bigcup_ {i = 1} ^ {| \mathcal {C} |} \mathcal {S} _ {i} \right|}{\sum_ {i = 1} ^ {| \mathcal {C} |} \left| \mathcal {S} _ {i} \right|}. \tag {11}
$$
Experiment Settings. We use BioBERT as the
13https://huggingface.co/dmis-lab/biobert-v1.1
14 When calculating Eqs. (8) and (9), to avoid log 0, we use  $P(w_{j},w_{k}) + \epsilon$  and  $P(w) + \epsilon$  to replace  $P(w_{j},w_{k})$  and  $P(w)$ , respectively, where  $\epsilon = 1 / |\mathcal{D}|$ .
PLM on SciDocs, and BERT-base-uncased as the PLM on Amazon and Twitter. The embedding dimension of  $\mathbf{u}_w$  is 768 (the same as  $\mathbf{e}_w$ ); the number of negative samples  $b = 5$ . In ensemble ranking, the length of the general/local ranking list  $M = 100$ ; the hyperparameter  $\rho$  in Eq. (6) is set as 0.1; the number of iterations  $T = 4$ ; after each iteration, we increase the size of  $S_i$  by  $N = 3$ . We use the top-10 ranked terms in each topic for final evaluation (i.e.,  $|S_i| = 10$  in Eqs. (8)-(11)). Experiments are run on Intel Xeon E5-2680 v2 @ 2.80GHz and one NVIDIA GeForce GTX 1080.
Table 3 shows the performance of all methods. We run each experiment 3 times with the average score reported. To show statistical significance, we conduct a two-tailed unpaired t-test to compare SEE-TOPIC and each baseline. (The performance of BERT and BioBERT is deterministic according to our usage. When comparing SEETOPIC with them, we conduct a two-tailed Z-test instead.) The significance level is also marked in Table 3.
We have the following observations from Table 3. (1) Our SEETOPIC model performs consistently well. In fact, it achieves the highest score in 8 columns and the second highest in the remaining 4 columns. (2) Classical seed-guided topic modeling baselines (i.e., SeededLDA and Anchored CorEx) perform not well in respect of NPMI (topic coherence) and MACC (term accuracy). Embedding-based topic discovery approaches (i.e., Labeled ETM and CatE) make some progress, but they still significantly underperform the PLM-empowered SEETOPIC model on SciDocs and Amazon. (3) SEETOPIC consistently performs better than SEE-TOPIC-NoIter on all three datasets, indicating the positive contribution of the proposed iterative process. (4) SEETOPIC guarantees the mutual exclusivity of  $S_{1}, \ldots, S_{|\mathcal{C}|}$ . In comparison, SeededLDA, Labeled ETM, and BERT cannot guarantee such mutual exclusivity.
In-vocabulary vs. Out-of-vocabulary. Figure 1 compares the MACC scores of different seed-guided topic discovery methods on in-vocabulary categories and out-of-vocabulary categories. We find that the performance improvement of SEE-TOPIC upon baselines on out-of-vocabulary categories is larger than that on in-vocabulary ones. For example, on Amazon, SEETOPIC underperforms CatE on in-vocabulary categories but outperforms CatE on out-of-vocabulary ones; on Twitter, the gap between SEETOPIC and baselines be
(a) SciDocs
(c) Twitter
Figure 1: MACC of seed-guided topic discovery methods on in-vocabulary categories and out-of-vocabulary categories.
(b) Effect of  $\rho$  on LCP
(a) Effect of  $\rho$  on NPMI
Figure 2: Parameter study of SEETOPIC measured by topic coherence.
(c) Effect of  $T$  on NPMI
(d) Effect of  $T$  on LCP
comes much more evident on out-of-vocabulary categories. Note that all baselines in Figure 1 do not utilize the power of PLMs, so this observation validates our claim that PLMs are helpful in tackling out-of-vocabulary seeds.
4.3 Parameter Study
We study the effect of two important hyperparameters:  $\rho$  (the hyperparameter in ensemble ranking) and  $T$  (the number of iterations). We vary the value of  $\rho$  in  $\{0.1, 0.3, 0.5, 0.7, 0.9, 1\}$  (SEETOPIC uses  $\rho = 0.1$  by default) and the value of  $T$  in  $\{1, 2, 3, 4, 5\}$  (SEETOPIC uses  $T = 4$  by default, and SEETOPIC-NoIter is the case when  $T = 1$ ). Figure 2 shows the change of model performance measured by NPMI and LCP.
Table 4: Top-5 representative terms retrieved by different algorithms for three out-of-vocabulary categories from SciDocs, Amazon, and Twitter.  $\checkmark$ : at least 3 of the 5 annotators judge the term as relevant to the seed.  $\pmb{x}$ : at most 2 of the 5 annotators judge the term as relevant to the seed.
Method	Top-5 Representative Terms
Dataset: SciDocs, Category Name: hepatitis a/b/c/e
SeededLDA	patients (X), treatment (X), placebo (X), study (X), group (X)
Anchored CorEx	expression (X), gene (X), cells (X), genes (X), genetic (X)
Labeled ETM	hepatitis b virus hbv dna (√), serum hbv dna (√), serum alanine aminotransferase (X), alanine aminotransferase alt (X), below detection limit (X)
CatE	chronic hepatitis b virus hbv infection (√), hepatitis b e antigen hbeag (√), hepatitis b virus hbv dna (√), normal alanine aminotransferase (X), hbeag-negative chronic hepatitis b (√)
BioBERT	hepatitis b virus hbv dna (√), chronic hepatitis b virus hbv infection (√), hepatitis b e antigen hbeag (√), hepatitis b virus hbv infection (√), chronic hepatitis c virus hcv (√)
SEETOPIC-NoIter	hepatitis b virus hbv dna (√), hepatitis b e antigen hbeag (√), chronic hepatitis b virus hbv infection (√), hepatitis b surface antigen hbsag (√), hbeag-negative chronic hepatitis b (√)
SEETOPIC	chronic hepatitis b virus hbv infection (√), hbeag-negative chronic hepatitis b (√), hepatitis c virus hcv-infected (√), hepatitis b virus hbv dna (√), chronic hepatitis c virus hcv (√)
Dataset: Amazon, Category Name: sports and outdoors
SeededLDA	use (X), good (X), one (X), product (X), like (X)
Anchored CorEx	sports (√), use (X), size (X), wear (X), fit (√)
Labeled ETM	cars and tracks (√), tracks and cars (√), search options (X), championships (X), cool bosses (X)
CatE	outdoorsmen (√), outdoor activities (√), cars and tracks (√), foot support (√), offers plenty (X)
BERT	cars and tracks (√), outdoor activities (√), outdoorsmen (√), sports (√), sporting events (√)
SEETOPIC-NoIter	outdoorsmen (√), outdoor activities (√), cars and tracks (√), indoor soccer (√), bike riding (√)
SEETOPIC	canoeing (√), picnics (√), bike rides (√), bike riding (√), rafting (√)
Dataset: Twitter, Category Name: travel and transport
SeededLDA	nyc (X), new york (X), line (√), high (X), time square (√)
Anchored CorEx	new york (X), post photo (√), new (X), day (X), today (X)
Labeled ETM	tourism (√), theview (√), file (X), morning view (√), gma (X)
CatE	maritime (√), tourism (√), natural history (X), scenery (√), elevate (X)
BERT	maritime (√), tourism (√), natural history (X), olive oil (X), baggage claim (√)
SEETOPIC-NoIter	maritime (√), tourism (√), natural history (X), scenery (√), navy (X)
SEETOPIC	wildlife (√), scenery (√), maritime (√), highlinepark (X), aquarium (√)
According to Figures 2(a) and 2(b), in most cases, the performance of SEETOPIC deteriorates as  $\rho$  increases from 0.1 to 0.9. Thus, setting  $\rho = 0.1$  always leads to competitive NPMI and LCP scores on the three datasets. Although  $\rho = 1$  is better than  $\rho = 0.9$ , its performance is still suboptimal in comparison with  $\rho = 0.1$ . This finding indicates that replacing the mean reciprocal rank (i.e.,  $\rho = 1$ ) with our proposed Eq. (6) is reasonable. According to Figures 2(c) and 2(d), SEETOPIC usually performs better when there are more iterations. On SciDocs and Twitter, the scores start to converge after  $T = 4$ . Besides, more iterations will result in longer running time. Overall, we believe setting  $T = 4$  strikes a good balance.
Finally, we show the terms retrieved by different methods as a case study. From each of the three datasets, we select an out-of-vocabulary category and show its topic discovery results in Table 4. We mark a retrieved term as correct  $(\checkmark)$  if at least 3 of the 5 annotators judge the term as relevant to the seed. Otherwise, we mark the term as incorrect  $(X)$ .
For the category "hepatitis  $a / b / c / e$  " from Sci-
Docs, SeededLDA and Anchored CorEx can only find very general medical terms, which are relevant to all seeds in SciDocs and thus inaccurate; Labeled ETM and CatE find terms about "alanine aminotransferase", whose elevation suggest not only hepatitis but also other diseases like diabetes and heart failure, thus not discriminative either; BioBERT and SEETOPIC, with the power of a PLM, can accurately pick terms relevant to "hepatitis  $b$  "and "hepatitis  $c$  . For the category "sports and outdoors" from Amazon, SeededLDA and Anchored CorEx again find very general terms, most of which are not category-discriminative; Labeled ETM and CatE are able to pick more specific terms such as "cars and tracks", but they still make mistakes; BERT, as a PLM, can accurately find terms that have lexical overlap with the category name (e.g., "outdoorsmen", "sporting events"), meanwhile such terms are less diverse; SEETOPIC-NoIter starts to discover more concrete terms than BERT (e.g., "indoor soccer", "bike riding") by leveraging local text semantics; the full SEETOPIC model, with an iterative updating process, can find more specific and informative terms (e.g., "canoeing", "picnics", and "rafting"). For the category "travel and trans
port" from Twitter, both BERT and CatE make mistakes by including the term "natural history"; SEETOPIC-NoIter, without an iterative update process, also includes this error; the full SEETOPIC model finally excludes this error and achieves the highest accuracy in the retrieved top-5 terms among all compared methods.
Seed-Guided Topic Discovery. Seed-guided topic models aim to leverage user-provided seeds to discover underlying topics according to users' interests. Early studies take LDA (Blei et al., 2003) as the backbone and incorporate seeds into model learning. For example, Andrzejewski et al. (2009) consider must-link and cannot-link constraints among seeds as priors. SeededLDA (Jagarlamudi et al., 2012) encourages topics to contain more seeds and encourages documents to select topics relevant to the seeds they contain. Anchored CorEx (Gallagher et al., 2017) extracts maximally informative topics by jointly compressing the corpus and preserving seed relevant information. Recent studies start to utilize embedding techniques to learn better word semantics. For example, CatE (Meng et al., 2020a) explicitly encourages distinction among retrieved topics via category-name guided embedding learning. However, all these models require the provided seeds to be in-vocabulary, mainly because they focus on the input corpus only and are not equipped with general knowledge of PLMs.
Embedding-Based Topic Discovery. A number of studies extend LDA to involve word embedding. The common strategy is to adapt distributions in LDA to generate real-valued data (e.g., Gaussian LDA (Das et al., 2015), LFTM (Nguyen et al., 2015), Spherical HDP (Batmanghelich et al., 2016), and CGTM (Xun et al., 2017b)). Some other studies think out of the LDA backbone. For example, TWE (Liu et al., 2015) uses topic structures to jointly learn topic embeddings and improve word embeddings. CLM (Xun et al., 2017a) collaboratively improves topic modeling and word embedding by coordinating global and local contexts. ETM (Dieng et al., 2020) models word-topic correlations via word embeddings to improve the expressiveness of topic models. More recently, Sia et al. (2020) show that directly clustering word embeddings (e.g., word2vec or BERT) also generates good topics; Thompson and Mimno (2020) further find that BERT and GPT-2 discover high-quality topics, but RoBERTa does not. These models are
unsupervised and hard to be applied to seed-guided settings. In contrast, our SEETOPIC framework joint leverages PLMs, word embeddings, and seed information.
In this paper, we study seed-guided topic discovery in the presence of out-of-vocabulary seeds. To understand and make use of in-vocabulary components in each seed, we utilize the tokenization and contextualization power of PLMs. We propose a seed-guided embedding learning framework inspired by the goal of maximizing PMI in topic modeling, and an iterative ensemble ranking process to jointly leverage general knowledge of the PLM and local signals learned from the input corpus. Experimental results show that SEETOPIC outperforms seed-guided topic discovery baselines and PLMs in terms of topic coherence, term accuracy, and topic diversity. A parameter study and a case study further validate some design choices in SEETOPIC.
In the future, it would be interesting to extend SEETOPIC to seed-guided hierarchical topic discovery, where parent and child information in the input category hierarchy may help infer the meaning of out-of-vocabulary nodes.
We thank anonymous reviewers for their valuable and insightful feedback. Research was supported in part by US DARPA KAIROS Program No. FA8750-19-2-1004, SocialSim Program No. W911NF-17-C-0099, and INCAS Program No. HR001121C0165, National Science Foundation IIS-19-56151, IIS-17-41317, and IIS 17-04532, and the Molecule Maker Lab Institute: An AI Research Institutes program supported by NSF under Award No. 2019897, and the Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) by NSF under Award No. 2118329. Any opinions, findings, and conclusions or recommendations expressed herein are those of the authors and do not necessarily represent the views, either expressed or implied, of DARPA or the U.S. Government.
David Andrzejewski, Xiaojin Zhu, and Mark Craven. 2009. Incorporating domain knowledge into topic modeling via dirichlet forest priors. In ICML'09, pages 25-32.
Kayhan Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, and Sam Gershman. 2016. Nonparametric spherical topic modeling with word embeddings. In ACL'16, pages 537-542.
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. JMLR, 3:993-1022.
Xingyuan Chen, Yunqing Xia, Peng Jin, and John Carroll. 2015. Dataless text classification with descriptive lda. In AAAI'15, pages 2224-2231.
Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S Weld. 2020. Specter: Document-level representation learning using citation-informed transformers. In ACL'20, pages 2270-2282.
Margaret H Coletti and Howard L Bleich. 2001. Medical subject headings used to search the biomedical literature. JAMIA, 8(4):317-323.
Rajarshi Das, Manzil Zaheer, and Chris Dyer. 2015. Gaussian lda for topic models with word embeddings. In ACL'15, pages 795-804.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In *NAACL-HLT'19*, pages 4171–4186.
Adji B Dieng, Francisco JR Ruiz, and David M Blei. 2020. Topic modeling in embedding spaces. TACL, 8:439-453.
Joseph L Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5):378.
Ryan J Gallagher, Kyle Reing, David Kale, and Greg Ver Steeg. 2017. Anchored correlation explanation: Topic modeling with minimal domain knowledge. TACL, 5:529-542.
Thomas L Griffiths and Mark Steyvers. 2004. Finding scientific topics. *PNAS*, 101(suppl 1):5228-5235.
Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In SIGIR'99, pages 50-57.
Jagadeesh Jagarlamudi, Hal Daumé, and Raghavendra Udupa. 2012. Incorporating lexical priors into topic models. In *EACL'12*, pages 204-213.
Jey Han Lau, David Newman, and Timothy Baldwin. 2014. Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality. In EACL'14, pages 530-539.
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234-1240.
Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. NIPS'14, pages 2177-2185.
Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. 2020. On the sentence embeddings from pre-trained language models. In EMNLP'20, pages 9119-9130.
Yang Liu, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. 2015. Topical word embeddings. In AAAI'15, pages 2418-2424.
Zequn Liu, Shukai Wang, Yiyang Gu, Ruiyi Zhang, Ming Zhang, and Sheng Wang. 2021. Graphine: A dataset for graph-aware terminology definition generation. In EMNLP'21, pages 3453-3463.
Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014. The stanford corenlp natural language processing toolkit. In ACL'14, System Demonstrations, pages 55-60.
Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: understanding rating dimensions with review text. In RecSys'13, pages 165-172.
Yu Meng, Jiaxin Huang, Guangyuan Wang, Zihan Wang, Chao Zhang, Yu Zhang, and Jiawei Han. 2020a. Discriminative topic mining via categoryname guided text embedding. In WWW'20, pages 2121-2132.
Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, and Jiawei Han. 2020b. Text classification using label names only: A language model self-training approach. In EMNLP'20, pages 9006-9017.
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS'13, pages 3111-3119.
David Mimno, Hanna Wallach, Edmund Talley, Miriam Leenders, and Andrew McCallum. 2011. Optimizing semantic coherence in topic models. In EMNLP'11, pages 262-272.
David Newman, Joy Han Lau, Karl Grieser, and Timothy Baldwin. 2010. Automatic evaluation of topic coherence. In *NAACL-HLT'10*, pages 100-108.
Dat Quoc Nguyen, Richard Billingsley, Lan Du, and Mark Johnson. 2015. Improving topic models with latent feature word representations. TACL, 3:299-313.
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In WSDM'18, pages 459-467.