2.1 Text classification with label noise

There are three main approaches to perform text classification with the presence of label noise: label noise-robust models, data cleansing methods and noise-tolerant methods (Frénay and Verleysen Reference Frénay and Verleysen2014).

Label noise-robust models are the simplest among all approaches. It neither tries to cleanse nor to model the noise. Certain types of classifiers are more robust to the label noise, such as ensembles using bagging (Dietterich Reference Dietterich2000). On the other hand, despite being a strong baseline for many classification tasks, support vector machine (SVM) is not robust to label noise (Nettleton, Orriols-Puig, and Fornells Reference Nettleton, Orriols-Puig and Fornells2010). The model relies on a few support vectors close to the decision boundary, and wrongly labelled data can have a large impact on the model’s accuracy. Label noise-robust models are relatively effective when the amount of label noise is small.

Data cleansing methods aim first to identify the label noises and then either remove the wrongly labelled data or try to reassign the correct label. Researchers favour data cleansing methods because they can be combined with any standard classification algorithm as an additional preprocessing step. Data cleansing methods are relatively easy to implement. We can either use anomaly detection methods (Sun et al. Reference Sun, Zhao, Wang and Chen2007) or model prediction-based filtering with either voting (Brodley et al. Reference Brodley and Friedl1996) or k-fold cross-validation (Gamberger, Lavrac, and Groselj Reference Gamberger, Lavrac and Groselj1999).

Last but not least, noise-tolerant methods try to learn a label noise model simultaneously with a classifier. This approach models the label noise explicitly using often a Bayesian prior (Swartz et al. Reference Swartz, Haitovsky, Vexler and Yang2004; Gerlach and Stamey Reference Gerlach and Stamey2007). In the same spirit, Breve, Zhao, and Quiles (Reference Breve, Zhao and Quiles2010) proposed a novel particle walk semi-supervised learning method which is robust to the noise of the label. Their method first converts the data set into a similarity graph and then applies a label propagation algorithm to correct the wrongly labelled instances.

2.2 Domain adaptation

There are two scenarios for domain adaptation, depending on whether there is in-domain labelled data (usually a small amount compared to the original data set) available.

When we have a small amount of in-domain labelled data, transfer learning is the standard approach (Pan and Yang Reference Pan and Yang2010). Transfer learning was popularised through the ImageNet challenges (Krizhevsky, Sutskever, and Hinton Reference Krizhevsky, Sutskever and Hinton2012). Recently, researchers replicated the success of transfer learning to the field of NLP and achieved new state-of-the-art results for text classification (Howard and Ruder Reference Howard and Ruder2018; Peters et al. Reference Peters, Neumann, Iyyer, Gardner, Clark, Lee and Zettlemoyer2018). These approaches first pre-train a language model on a large corpus and then fine-tune the language model using the in-domain unlabelled data. While in-domain labelled data are usually expensive to obtain, unlabelled data such as movie reviews or web pages are often available in abundance. The final step is to train a classifier for the target classification task using the fine-tuned encoder from the previous step. Transfer learning significantly reduced the labelled data needed to train classifiers with decent accuracy. Howard and Ruder (Reference Howard and Ruder2018) demonstrated that with 100 labelled examples, they could match the performance of training from scratch on 100x more data.

When there is no in-domain labelled data at all, we need to build models that are ‘domain-robust’ or tap on unsupervised learning methods. Sachan, Zaheer, and Salakhutdinov (Reference Sachan, Zaheer and Salakhutdinov2018) investigated various models’ reliance on key lexicons by carefully constructing training and testing data sets with not key lexicon overlap. They found out while sophisticated deep learning models can theoretically capture non-local semantic features, they still rely heavily on the presence of keywords in practice. On the lexicon data set, the accuracy of various models dropped on average 10–20%. To reduce this gap, Sachan et al. (Reference Sachan, Zaheer and Salakhutdinov2018) proposed two methods, namely keyword anonymisation and adaptive word dropout to regularise the model and make it rely less on the keywords. Similarly, Li et al. (Reference Li, Zhou, Ji, Duan and Chen2018b) performed adversarial training with Gradient Reversal Layer (Ganin et al. Reference Ganin, Ustinova, Ajakan, Germain, Larochelle, Laviolette, Marchand and Lempitsky2016) to remove category-specific information and to make the model generalise to unseen categories.

Mudinas et al. (Reference Mudinas, Zhang and Levene2018) proposed a novel unsupervised method to bootstrap domain-specific sentiment classifiers. They observed that the positive/negative sentiment words form distinct clusters in the in-domain embedding space. To this end, they trained a simple linear model to classify words into positive or negative sentiment based on their word embedding alone. Then, they used the induced lexicon to assign pseudo-labels to the unlabelled documents. Finally, the pseudo-labelled documents were used to train a supervised long short-term memory model which achieves accuracy comparable to fully supervised approaches.

2.3 Dataless classification

Dataless classification (Chang et al. Reference Chang, Ratinov, Roth and Srikumar2008) denotes the family of learning protocols which can induce classifiers without any labelled data (document). Instead, dataless classification algorithms make use of labelled keywords and unlabelled corpus to train the classifier. There are various approaches for dataless classification, such as hand-crafted rules, constraint optimisation, injecting the keywords as priors to the model and semantic representation of the documents and the labels.

Chang et al. (Reference Chang, Ratinov, Roth and Srikumar2008) made use of Explicit Semantic Analysis (ESA) (Gabrilovich et al. Reference Gabrilovich and Markovitch2007) to embed both the documents and the labels into a shared semantic space representing concepts inferred from Wikipedia. The classification is performed by calculating the cosine similarity between the document and the label representation. They also considered the impact of dataless classification on domain adaptation. However, differing from this work, they only considered the binary classification between two categories ‘baseball’ and ‘hockey’, and their source and target data set (20NG and Yahoo! Answers data set) are both manually curated and do not contain label noise. Subsequent work on dataless classification extended the ESA approach to both hierarchical classification (Song and Roth Reference Song and Roth2014; Zheng et al. Reference Zheng, Tian, Hu, Iyer and Sycara2016) and cross-lingual classification (Song et al. Reference Song, Upadhyay, Peng and Roth2016; Song et al. Reference Song, Upadhyay, Peng, Mayhew and Roth2019).

Druck, Mann, and McCallum (Reference Druck, Mann and McCallum2008) proposed generalised expectation (GE) criteria which induce a classifier by performing constraint optimisation over the distribution of labelled words among documents predicted into each category. GE has been successfully applied on different tasks, such as text categorisation (Druck et al. Reference Druck, Mann and McCallum2008) and language identification in mixed-language documents (King and Abney Reference King and Abney2013). Similarly, Charoenphakdee et al. (Reference Charoenphakdee, Lee, Jin, Wanvarie and Sugiyama2019) proposed a theoretically grounded risk minimisation framework that directly optimises the area under the receiver operating characteristic curve (area under the curve) of a dataless classification model.

Settles (Reference Settles2011) and Li and Yang (Reference Li and Yang2018) both used multinomial naïve Bayes (MNB) for dataless classification. Settles (Reference Settles2011) extended MNB to allow labels for words by increasing their Dirichlet prior. His method consists of three steps: first to estimate the initial parameters using only the priors; second to apply the induced classifier on unlabelled documents; lastly to re-estimate the model parameters using both labelled and probabilistically-labelled documents. Using an interactive approach to query document and word labels from the user, the system can achieve 90% of state-of-the-art performance after a few minutes of annotation. In contrast, Li and Yang (Reference Li and Yang2018) used the labelled keywords to provide pseudo-labelled documents. They then performed standard semi-supervised learning using expectation maximization algorithm.

Li et al. (Reference Li, Xing, Sun and Ma2016) proposed Seed-Guided Topic Model (STM) for dataless text classification. Different from the standard latent Dirichlet allocation, STM models, two sets of topics: category topics and general topics. Category topics contains specific words which are representative of a category. General topics are words which frequently occur in a category, but they alone do not indicate the category. For example, if a document contains the keyword ‘mammogram’, it is almost certainly related to cancer. However, it is not the case for keywords like ‘breast’ and ‘prostate’, although they do frequently occur in documents about ‘cancer’. The inference of STM consists of two stages: they first initialise the category word probability and the document category distribution by counting the co-occurrence with seed words belonging to each category. Then, they apply joint Gibbs sampling to infer all the hidden parameters. STM is demonstrated to drastically outperform various baselines, including GE and a naïve Bayes model similar to Settles (Reference Settles2011). STM has also been extended to perform multi-label classification (Zha and Li Reference Zha and Li2019) and joint document filtering and classification (Li et al. Reference Li, Chen, Xing, Sun and Ma2018a).

Recently, Meng et al. (Reference Meng, Shen, Zhang and Han2018) proposed WeSTClass, a novel weakly supervised text classification method. It consists of two steps: pre-training and self-training. First, it generates pseudo documents for each category from various sources of supervision, such as labelled keywords or documents. A generative mixture model is used to repeatedly generate a number of terms from a background distribution and the class-specific distribution to form pseudo documents. The pseudo documents are used to pre-train a neural model. To adapt to real-world input documents, it performs self-training on unlabelled real documents and automatically adds the most confident predictions to the training set. The method drastically outperformed baselines such as information retrieval with tf-idf, Chang et al. (Reference Chang, Ratinov, Roth and Srikumar2008) and convolutional neural networks (CNN) trained on pseudo-labelled documents.

Another task closely related to dataless classification is zero-shot text classification (0SHOT-TC) (Yin, Hay, and Roth Reference Yin, Hay and Roth2019). Besides allowing no labelled training documents, it requires the model to generalise to unseen labels. For example, the classifier is trained on ‘hockey’ and ‘baseball’ category using either a supervised or dataless learning method, and it needs to classify documents belonging to the ‘badminton’ category which occurs only at test time. The main approach to 0SHOT-TC is to calculate the interaction between the document and category embeddings and model it as either a ranking or classification problem.

Li et al. (Reference Li, Chen, Xing, Sun and Ma2018) calculated the element-wise difference and element-wise product between the category embedding and each word in the document. The document-level relevance is aggregated using convolutional layers. Nam, Menca, and Fürnkranz (Reference Nam, Menca and Fürnkranz2016) applied a bilinear function f(x,y) in the form of x^TWy, where x is the document representation, y is the label representation and W is a matrix with learnable parameters capturing the interaction between the two representations. Pappas and Henderson (Reference Pappas and Henderson2019) proposed generalised input-label embedding (GILE), which extends the bilinear interaction with a more generalised interaction with a non-linear activation function and a controllable parameter capacity. They demonstrated that GILE outperformed the model proposed by Nam et al. (Reference Nam, Menca and Fürnkranz2016) drastically for both seen and unseen labels.

We believe 0SHOT-TC is a promising research direction. However, its requirement of generalising to any unseen labels limits the accuracy it can achieve with the current state of research. The state-of-the-art model’s performance on unseen labels is 243–1062% worse than on seen labels based on different evaluation metrics (Pappas and Henderson Reference Pappas and Henderson2019). In contrast, dataless classification models can often yield performance that is close to a fully supervised model. Besides, it is reasonable to assume that we know the categories in advance before we deploy the classifier. Even if the list of categories is non-static, we can easily retrain the dataless classifier with the new list of category names and keywords. Therefore, we limit the scope of this work to dataless classification and do not consider the zero-shot learning setting.

3.1 Mining keywords from (noisy) labelled corpus

The selection of keywords makes a significant impact on the accuracy of the induced dataless classifier (Li et al. Reference Li, Chen, Xing, Sun and Ma2018a). While keyword (or keyphrase) extraction from text has been extensively studied, how the selection of keywords impacts dataless classification was rarely if ever discussed. Previous work used either hand-picked keywords (Druck et al. Reference Druck, Mann and McCallum2008; Settles Reference Settles2011; Meng et al. Reference Meng, Shen, Zhang and Han2018) or relied on only the category name or category description (Chang et al. Reference Chang, Ratinov, Roth and Srikumar2008; Li et al. Reference Li, Xing, Sun and Ma2016; Li et al. Reference Li, Zhou, Ji, Duan and Chen2018b). The problem of extracting keywords from a (noisily) labelled corpus is defined formally as follows.

We have a corpus (D ₁,…, D_C), where D_c = (d ₁,…, d_k) is the set of documents (noisily) labelled as category c. Each document d_i contains a list of terms (w ₁,…, w _l). We want to generate a list of representative terms t ₁,…, t_n from the vocabulary V = [w ₁,…, w_N] for each category. This is related to the measurement of association in information theory. Therefore, we try to apply pointwise mutual information between keyword w and category c. pmi(w;c) is defined as follows:

(1) \begin{equation}pmi(w;\ c) \equiv {\rm log} \frac{p(w,c)}{p(w)p(c)} = {\rm log} \frac{df(w,c)\sum_{c \in C}{df(c)}}{df(w)df(c)}\end{equation}

where df(w,c) is the number of documents belong to category c and contain word w and df(w) is the number of documents that contain word w. df(c) is the number of documents belonging to category c. Correspondingly, $\sum_{c \in C}{df(c)}$ is the total number of documents in the corpus. We notice that pmi tends to favour rare words. For example, when a word occurs only once in the corpus, it will have high pmi score in the category where it occurs. This makes the mined keywords unreliable, especially in the presence of the label noise. We therefore introduce pmi-freq with two modifications: first, we multiple the pmi score with the log-term frequency of word w. Second, we set a threshold of minimum term frequency of 5. The pmi-freq will be set to zero if the term frequency is below the threshold.

(2) \begin{equation}pmi\text{-}freq(w;\ c)=\begin{cases} {\rm log}\,df(w){\rm log}\frac{df(w,c)\sum_{c \in C}{df(c)}}{df(w)df(c)},& \text{if } df(w)\geq 5\\[4pt] 0, & \text{otherwise}\end{cases}\end{equation}

While pmi ensures that there is a strong association between the top keywords and the category, we also want the keywords for different categories to have little or no overlap. We apply maximal marginal relevance (MMR) (Carbonell and Goldstein Reference Carbonell and Goldstein1998) to achieve this.

(3) \begin{equation}mmr \equiv arg\max_{w_i \in S_{c_m}}[\lambda\,Sim_1(w_i, c_m) - (1-\lambda)\max_{w_j \in S_{c_n} \& m \neq n}Sim_2(w_i, w_j)]\end{equation}

In Equation (3), the first term measures the similarity between candidate word w_i and category c_m. The second term measures the maximum similarity between candidate word w_i and any seed word from another category c_n. The parameter λ controls the weights of the two terms. A higher λ favours keywords that are strongly associated to category c_m. A smaller λ favours keywords that occur exclusively in category c_m but not in other categories. We use a default λ of 0.5 and pmi-freq as the similarity measure for both Sim ₁ and Sim ₂.

We want to study the impact of different label noise rate on the quality of the mined keywords. Since the label noise rate for a corpus is fixed, we synthesise label noise using the following mechanism:

1. 1. choose the label noise rate ∊ to generate noise;

2. 2. calculate the # of docs with corrupted label: $n_{corrupt} = math.floor(n_{docs} \cdot \epsilon)$; and

3. 3. randomly select n_corrupt docs and randomly shuffle their labels;

We use the well-known 20 newsgroups data set (Lang Reference Lang1995) and vary the percentage of label noise from 0% up to 70%. We manually examine the top 10 keywords to evaluate the quality of the keyword mining algorithm. We count a keyword to be correct if it unambiguously represents the category. For example, while the word ‘Israeli’ represents the category ‘talk.politics.mideast’, the word ‘territories’ does not. Due to the space limit, we only show the results for three randomly selected categories (talk.politics.mideast, rec.autos, rec.sport.baseball), while the other categories follow the same trend.

Among previous work, only Druck et al. (Reference Druck, Mann and McCallum2008) proposed an automatic algorithm to mine keywords from oracle-labelled documents based on mutual information (mi). Therefore, we compare the three aforementioned methods pmi, pmi-freq, and mmr together with mi. Besides, we also show the result of a naïve baseline freq, which outputs the most frequent word for each category after stop word removal. mi is expressed as

(4) \begin{equation}mi(w;C) \equiv \sum_{c \in C}p(w,c){\rm log}\frac{p(w,c)}{p(w)p(c)} \propto \sum_{c \in C}df(w,c) {\rm log} \frac{df(w,c)\sum_{c \in C}{df(c)}}{df(w)df(c)}\end{equation}

mi is independent from the category since it sums up all the categories. Therefore, Druck et al. (Reference Druck, Mann and McCallum2008) first selected the most predictive k features based on mi and then assigned the word to the category where it occurs with most often, and other categories that it occurs with at least half as often.

Figure 1 shows the number of correct keywords output by each algorithm for different noise rate ∊. We can observe that pmi-freq and mmr almost always generate better keywords than pmi except for the automotive category when the label noise rate is relatively low. This is because pmi generates specific automotive brand names which are relatively unambiguous. pmi-freq and mmr remain effective even when the label noise rate is 0.5. They also tend to be more robust against the change of the noise rate, and the generated keywords remain relatively static. On the other hand, pmi sometimes generates completely different keywords when the noise rate is increased by 0.1.

Table 1 shows the generated keywords of various algorithms for the category ‘rec.sport.baseball’ with a label noise rate of 0.3. We underline the ambiguous keywords. We can see that almost all keywords generated by pmi are person or team names. Of 10 keywords, 6 are ambiguous. In contrast, pmi-freq and mmr generate specific keywords related to the baseball game. They generate much fewer ambiguous keywords and will likely generalise better. The two baselines freq and mi both perform poorly. While freq tends to generate common words like ‘bad’ and ‘actually’, mi tends to generate words that occur frequently in multiple categories (because it sums up the mutual information score for all categories) and therefore have less discriminative power.

Figure 1. Number of correct keywords generated by each algorithm varying the percentage of label noise.

The full list of keywords generated by each algorithm at different label noise rate is presented in Appendix A. pmi-freq and mmr perform on par with each other. Therefore, we choose to use pmi-freq as the final algorithm to mine keywords due to its simplicity.

Table 1. Automatically mined keywords for the category ‘rec.sport.baseball’ with 0.3 label noise. The ambiguous keywords are underlined

3.2 Training dataless classifiers

We apply the STM (Li et al. Reference Li, Xing, Sun and Ma2016) to train dataless classifiers. The architecture of STM is depicted in Figure 2. STM takes labelled seed words and unlabelled documents as input. In the first step, the initial document category distribution is estimated from the term frequency of seed words with a Dirichlet smoothing prior. It also calculates the category word probability of unlabelled words based on their co-occurrence with the labelled seed words as follows:

Figure 2. The architecture of the seed-word-guided topic model.

We first calculate the conditional probability p(w|s) using Equation (5), where df(w,s) is the number of the documents containing both unlabelled word w and seed word s. The relevance of word w to category c is then calculated as the average conditional probability of w with respect to each seed word in $\mathbb{S}_c$ (Equation (6)).

(5) \begin{equation}p(w|s) = \frac{df(w,s)}{df(s)}\end{equation}

(6) \begin{equation}rel(w,c) = \frac{1}{|\mathbb{S}_c|}\sum_{s \in \mathbb{S}_c}p(w|s)\end{equation}

Lastly, the relevance score is normalised by summing over each category c and each word w in the vocabulary in Equations (7) and (8), respectively. The final ν_c values are used to initialise the category word probability before the inference process.

(7) \begin{equation}\nu(w,c) = {\rm max}\left(\frac{rel(w,c)}{\sum_{c}rel(w,c)}-\frac{1}{c}, 0\right)\end{equation}

(8) \begin{equation}\nu_c(w,c) = \frac{\nu(w,c)}{\sum_w\nu(w,c)}\end{equation}

The model differentiates two types of underlying topics: the category topic and the general topic. General topics capture the global semantic information and are shared by all the documents. A category topic is associated with a single category and captures the relevant keywords of the category. STM model introduces a binary variable x_d,i which indicates whether the associated word w_d,i is generated from document d’s category topic c_d or from one of the general topics. The parameter inference is carried out using Gibbs Sampling, and the generative process is described below:

1. 1. for each category c ∈ {1 … C},

   1. a) draw a general-topic distribution φ ∼ Dirichlet(α ₀) and

   2. b) draw a category word distribution θ ∼ Dirichlet(β ₀);

2. 2. for each general topic t ∈ {1 … T},

   1. (a) draw a word distribution for the general topic φ_t ∼ Dirichlet(β ₁);

3. 3. For each document d ∈ {1 … D},

   1. (a) generate an initial category distribution η_d;

   2. (b) draw category $c_d\sim Multinomial(\eta_d)$;

   3. (c) draw a general-topic distribution $\theta_d\sim Dirichlet(\alpha_1\cdot \varphi_{c_d})$;

   4. (d) for each word i ∈ {1 … |d|},

      1. i. draw $x_{d,i}\sim Bernoulli(\delta_{w_{d,i},c_d})$;

      2. ii. if x_d,i = 0: draw word $w_{d,i}\sim Multinomial(\vartheta_{c_d})$;

         if x_d,i = 1:

         1. A. draw general-topic assignment $z_{d,i}\sim Multinomial(\theta_d)$;

         2. B. draw word $w_{d,i}\sim \phi_{z_{d,i}}$.

During the inference/prediction, the model first jointly samples each pair of x_d,i and z_d,i conditioned on every possible category c. It then estimates the conditional probability distribution $p(c_d=c|\mathbf{z},\mathbf{x}, \mathbf{c_{\neg d}}, \mathbf{w})$, where ¬d denotes the collection of documents excluding document d. We observe that STM tends to predict inconsistent labels for the same input. In the work of Li et al. (Reference Li, Xing, Sun and Ma2016), they predict the category as the category sampled from the category probability distribution in the last iteration. Instead, we predict ${\rm argmax}\: p(c_d=c|\mathbf{z},\mathbf{x}, \mathbf{c_{\neg d}}, \mathbf{w})$ in the last iteration as the document category.

The STM model has two main advantages that allow it to achieve high accuracy for dataless classification. First, the model explicitly calculates the correlation of unlabelled words to the seed words and uses it to initialise the category word probability. This makes the inference process much easier compared to using a randomly initialised probability distribution. Second, by separating the topics into general topics and category topics, the model can focus on only the reliable signals and ‘skim-through’ the rest of the document by assigning it to a general topic.

We apply the STM model with the keywords mined using the algorithm described in Section 3.1 and a large unlabelled corpus to train the final classifier.

4.1 Data sets

We use three data sets to carry out experiments in this work. One is a legacy large labelled data set crawled from newswire sites. We refer to this data set as news-crawl data set. The label was obtained by mapping the news categories to IAB categories. Therefore, we expect the presence of label noise in the data set. We also crawled another evaluation data set with roughly one hundred documents per category following similar methodology. We refer to it as news-crawl-v2 data set. These two data sets differ in two ways. First, news-crawl data set was collected before April 2015, and news-crawl-v2 data set was collected during May 2019. Second, they are crawled from different websites. These differences allow us to study the behaviour of the models when applied to a slightly different domain. The details of constructing the data set as well as the websites where the two data sets were collected are presented in Appendix B.

The third data set is a small manually labelled evaluation data set. We crawled the documents from URLs in the real-time-bidding (RTB) requests we logged. The RTB traffic contains URLs in the user browsing history where there is an opportunity for us to display ads. It contains heterogeneous web pages, such as forums, blogs and even social network sites. This data set is more realistic to our application domain. We refer to this data set as browsing data set. All data sets contain the same 22 categories (all IAB tier-1 categoriesFootnote d except for ‘News’ because ‘News’ can cover any topic). We release the evaluation data sets publicly for researchers to reproduce our results and to facilitate future research in contextual text classification.Footnote e

For the browsing data set, we found out during the annotation that some documents may belong to multiple categories. Therefore, we did not limit to one category per document but labelled all the correct categories. Table 2 summarises the number of labels assigned to documents. Sixty per cent of the documents were assigned only one label, and ninety-four per cent of the documents have two or fewer labels. Multi-label classification is beyond the scope of this work. We are only interested in predicting one of the correct labels which have been annotated.

Table 2. The number of labels assigned to documents in the browsing data set

The number of documents in the three corpora is shown in Table 3. We can see that the number of documents for each category is imbalanced, especially for the news-crawl data set. We did not downsample the majority categories but kept all the documents that we crawled. Another observation is that the categories with the most number of documents in the news-crawl data set and browsing data set are very different. While the news-crawl data set contains many documents related to business or politics, in the user browsing data set, there are more documents related to entertainment, food&drinks and shopping. Besides, there is also a difference in the document length in the data sets. The average document lengths for the news-crawl data set and news-crawl-v2 data set are 503 and 1470 words, while in the user browsing data set, it is 350 words. This evidence suggests a potential mismatch between the training data and the real-world data where the model is applied.

Table 3. Statistics of data sets. The categories are sorted by the number of documents in the news-crawl corpus

4.2 Experimental setup

We split news-crawl data set randomly into a 0.9/0.1 training and testing set. The fixed test set is only used for evaluation and not included during training and keyword mining. News-crawl-v2 and browsing data set are reserved for evaluation only.

4.2.3 Performance metrics

For the news-crawl data set, we report the accuracy and Macro-F ₁ scores. Because the categories are highly imbalanced, Macro-F ₁ is more meaningful than Macro-F ₁ to indicate the average performance across different categories.

For the browsing data set, because some documents contain multiple labels, we cannot apply standard multi-class classification metrics directly. Therefore, we use accuracy⁺ and maF ₁ for multi-label classification following Nam et al. (Reference Nam, Menca, Kim and Fürnkranz2017).

Accuracy⁺ is defined as the total correct predictions divided by the total predictions. Since all the models are multi-class classification models and predict only one label, we count the prediction to be correct if the predicted label is one of the labels that has been annotated by the human annotator. maF ₁ for multi-label classification is defined as

(9) \begin{equation}\text{ma}F_1 = \frac{1}{L}\sum_{j=1}^{L} \frac{2tp_j}{2tp_j + fp_j + fn_j}\end{equation}

where L is the number of categories and tp_j, fp_j, and fn_j denote the number of true-positive, false-positive and false-negative of category j. We note that to obtain a perfect maF ₁ score of 1, the model needs to predict all the correct label(s) for each document. Since all the models in comparison predict only one label for each document, the maF ₁ score is strictly lower than 1, but the comparison is still fair nevertheless.

4.3 Results and discussion

4.3.1 Mined keywords from labelled corpus

Table 4 shows the generated keywords for each category, which will be used by all dataless classification models.

Table 4. Generated keywords using pmi-freq

Table 5. Performance of various models on three data sets: news-crawl test set, news-crawl-v2 data set and on browsing data set. The best results on each data set are in boldface

4.3.2 Text classification performance

Table 5 shows the performance of various models on the three data sets. All the supervised learning models are trained on the full news-crawl training set with class labels. All the dataless classification models are trained using the same list of keywords in Section 4.3.1. They also access the full news-crawl training set but without the class labels.

All the supervised models performed reasonably well on the news-crawl test set. However, their performance degraded drastically on the other two data sets. This shows that while supervised learning methods can learn important features from the training data and predict accurately on similar documents, there is no guarantee that the model will perform well when the input document looks very different, although they are about the same topics. The recent state-of-the-art ULMFiT model outperformed the other baselines by a large margin on news-crawl test set and achieved a high accuracy of 0.922. Its performance on the other data sets is still competitive among the supervised baselines but lags behind the best dataless classification models.

While MNB’s performance on the news-crawl test set lagged behind SVM, its performance on the other two data sets was superior, suggesting that it generalises better to a new domain different from the training data. This is consistent with the finding of Sachan et al. (Reference Sachan, Zaheer and Salakhutdinov2018), where a discriminative Logistic Regression model suffered more than a Naïve Bayes model when applying on a corpus without important lexicon overlap. The KNN model obtained a slightly inferior yet still reasonable performance on the news-crawl test set. However, it failed on the other two data sets. Suggesting the differences between the data sets are large, and a similarity-based classification algorithm will not work.

It is interesting to observe that while dataless classification models lagged behind all the supervised learning models on the news-crawl test set, they yielded competitive performance on the other two data sets. This confirms our intuition that by abstracting the semantics using keywords, we can obtain better transferability.

STM achieved the best performance on all three data sets compared to other dataless baselines. GE is the second best model, and its performance is consistently 1–3% lower than STM. Despite the two models have completely different architectures, they both explicitly exploit the word-occurrence information, suggesting that it is an important strategy to bootstrap knowledge in a dataless learning process.

The doc2vec baseline has a mediocre performance on all data sets and is consistently 15–20% lower than STM. WeSTClass’s performance was surprisingly very low. Meng et al. (Reference Meng, Shen, Zhang and Han2018) demonstrated that the model performed well on binary sentiment classification and topic classification with a small number of categories (four or five). However, our task has 22 categories. The core assumption of WeSTClass that the keywords and documents of each category lie in disjoint clusters in a low-dimensional space may not hold when the number of categories get larger. This is partially validated by the poor performance of doc2vec, where the average of the keyword embeddings is used to represent the category. After pretraining with pseudo-labelled documents, WeSTClass has a poor macro-F ₁ score of 0.104, suggesting the poor quality of the pseudo documents. The self-training does improve macro-F ₁ by nearly 6%, but WeSTClass’s performance remains very poor compared to other baselines.

Figure 3. The confusion matrix of STM model prediction on the news-crawl test set. The diagonal entries (correct predictions) have been removed to surface the misclassifications.

Two questions arose naturally when we were analysing the result:

1. 1. Why STM performs well on different data sets but not so well on the test set which is most similar to the data which it is trained on?

2. 2. What caused ULMFiT’s performance to degrade drastically when applied on another domain?

To answer the first question, we plot the confusion matrix of STM model on news-crawl test set in Figure 3. We can see that the misclassifications are not random. While we anticipate misclassifications among closely related categories such as ‘business’ and ‘personal-finance’, some other cases are worth investigating, such as misclassifying a large proportion of ‘pets’ documents to ‘family-parenting’.

To this end, we did further analysis on the documents belonging to the ‘pets’ category that are misclassified as other categories. Table 6 shows the top five categories of STM model predicted for documents belonging to the ‘pets’ category. Our first impression is that these categories are somehow related to ‘pets’, such as pets are part of the family and important especially to children. Some articles may also talk about veterinary medicine or pet-related diseases, thus making it related to ‘health & fitness’ category.

Table 6. Top five categories the STM model predicts for documents belonging to pets category

We inspected the documents which are ‘misclassified’ as ‘family & parenting’ and found that almost all of them are related to children and pets or pets in a family/relationship. Table 7 shows some example snippets. These documents naturally belong to both categories, but only one label was assigned in the news-crawl data set. This explains why STM’s performance on the news-crawl test set is poor, while it performs well on the browsing data set, where all correct categories are labelled.

Table 7. Example documents with label ‘pets’ and are classified as ‘family & parenting’

To understand why ULMFiT’s performance dropped significantly when applied on a different domain, we want to understand what features the model learns and whether these features can be easily transferable across domains. Therefore, we used LIMEFootnote o (Ribeiro, Singh, and Guestrin Reference Ribeiro, Singh and Guestrin2016), a model-agnostic interpretation technique to explain the predictions of ULMFiT on sample text drawn from different data sets. LIME perturbs the input instance X slightly and probes the classifier for prediction. It then fits a linear interpretation model that approximates the classifier locally in the vicinity of X. LIME can output a list of features contributing to predicting each category with their feature importance.

Figure 4 shows LIME’s explanations for both ULMFiT and STM on a sample document from news-crawl data set.Footnote p While both models predict close to 1.0 probability for the correct category ‘home-garden’, the interpretation for STM is obviously more plausible. In contrast, Figure 5 depicts an example from browsing data set where STM predicts the correct label with high confidence but ULMFiT predicts the wrong label. In general, we found that ULMFiT tends to focus on more ‘fuzzy’ features. This may due to the nature of deep learning models which capture complex interactions of non-local features. While these features helped ULMFiT to achieve a very high accuracy on a random-split test set, they may not remain reliable when the input data differ significantly from the training data.

Figure 4. LIME explanations on a sample document from news-crawl data set.

Figure 5. LIME explanations on a sample document from browsing data set.

4.3.3 Impact of keyword selection strategy

In Section 3.1, we manually evaluated the quality of the mined keywords using different algorithms. In this section, we try to answer the question how much different keyword selection strategies affect the accuracy of the induced dataless classifier and whether our proposed keyword selection method improves the final accuracy.

To this end, we trained STM with different set of keywords and evaluated them on the same evaluation data sets. The keyword selection strategies we compared with are S_label, which uses only the words occurring in the category name. It is one of the systems used in Li et al. (Reference Li, Xing, Sun and Ma2016). We also compare with S_freq and S_mi, which use frequency-based and mutual information-based keyword selection mentioned in Section 3.1. For S_freq and S_mi, we generate 15 top keywords for each segment, making the number of keywords equal to S_pmi-freq. We publish the keywords selected using each method to facilitate replication of our results.Footnote q

Table 8 shows the result. First, using the seed words occurring in the category name alone resulted in the poorest performance, indicating that the category name does not provide sufficient supervision to train an accurate classifier. Some category names are covering terms which might not frequently occur in the text, such as ‘technology & computing’, where people might talk much more about ‘mobile phones’ and ‘laptops’ than ‘computing’. The baseline seed word selection methods S_freq and S_mi improved from S_label. However, their performance still far lagged behind the proposed S_pmi-freq, demonstrating the importance of the seed word selection method on the final accuracy of the dataless classifier.

Table 8. Performance of STM using different set of keywords on three data sets: news-crawl test set, news-crawl-v2 data set and browsing data set

Table 9. Sample generated keywords using mi and the P/R/F₁ on the news-crawl-v2 data set

Interestingly, we observed that mi did generate good keywords for some categories but failed for some other categories. We show two sample categories in Table 9 with their corresponding keywords selected by mi and their P/R/F₁ score on the news-crawl-v2 data set. Most keywords mi selected for the category ‘real estate’ turned out to be location names in Singapore. This might be due to the bias in the data collection. As a result, the category did not generalise at all and had a zero F₁ score on the news-crawl-v2 data set. On the other hand, mi generates good keywords for the category ‘family & parenting’, and the F₁ score is also high. It demonstrates that mi is to some extent effective to detect meaningful keywords. However, it is not robust enough to guarantee good-quality keywords for each category.

Putting together the result in this section and Section 4.3.2, we want to highlight that the selection of seed words has at least as much impact on the dataless classification accuracy as the selection of different algorithms. However, it has not received due attention from the research community.

4.4 Experiment on sentiment classification data sets

To demonstrate the effectiveness of the proposed method on other tasks and data sets, we conduct an experiment on a pair of publicly available sentiment classification data sets. We train the supervised and dataless classifiers on the IMDB data set (Maas et al. Reference Maas, Daly, Pham, Huang, Ng and Potts2011) and evaluate the model on both IMDB and Yelp test set (Zhang, Zhao, and LeCun Reference Zhang, Zhao and LeCun2015). Both data sets consist of evenly distributed binary polarity classes. IMDB data set contains 25,000 training documents, 25,000 testing documents and an additional 50,000 unlabelled documents. Yelp test set contains a total of 38,000 documents. Compared to the previous data sets for contextual advertising, these data sets are easier for three reasons: they contain only two classes; the classes are balanced and the document labels are free from noise.

We use IMDB training set to train all the supervised classifiers.Footnote r We also use the training set to automatically mine 30 keywords for each class, which are used by all dataless classifiers. The list of keywords are shown in Table 10. We then use the combination of the training set and the unlabelled set to train the dataless classifiers. Table 11 shows the results of all the competing models. Since the data sets contain only two evenly distributed classes, we report only the accuracy score.

Table 10. Generated keywords using pmi-freq from IMDB training set

Table 11. Accuracy of various models on IMDB and Yelp test set. The best results on each data set are in boldface

We can make a few observations from the result. First, some dataless classification models perform on par with simple supervised learning baselines. This is encouraging because the supervised models are trained using 25,000 labelled documents, while the dataless classification models use only 30 automatically mined keywords per category. Second, ULMFiT performs the best on both data sets. This is probably because the training data are free from noise, and the two data sets are relatively similar (both are user reviews).

While dataless classification models do not perform as well as a state-of-the-art supervised model on sentiment classification data sets, they do demonstrate better robustness when applied to a different domain. On average, supervised models’ accuracy dropped 7.5% when applied to Yelp data set compared to IMDB test data set. On the other hand, dataless classification models’ accuracy dropped only 3.6%.

We believe the performance of dataless classification models versus supervised models is related to the bias-variance tradeoff. Dataless classification models have high bias resulting from the labelled keywords, but low variance, and can generalise better to samples that look different. Supervised models, especially deep learning models, have much lower bias but high variance. It gives us the hint that dataless classification models might perform better than supervised learning models when the document labels contain a lot of noise or the training and testing samples look very different (high covariate shift).

In this set of experiments, STM does not perform as well as GE and MNB/Priors. STM applies topic modelling to capture latent topics in the background corpus. It might be more suitable for topic classification rather than sentiment classification. In the previous topic classification experiments on contextual advertising data sets, despite the input source changes, the underlying topics remain similar and therefore the inferred topics are useful across different data sets. However, the IMDB data set consists of movie reviews, and the Yelp data set consists of reviews for points of interest such as restaurants and hotels. The topics in the two data sets are completely different, suggesting that the latent topics STM inferred from the IMDB data set are not transferable to the Yelp data set. This explains why STM’s accuracy dropped close to 8% on the Yelp data set.

The underlined keywords in Table 10 are considered low quality. They consist of movie or actor names (‘redeeming’ and ‘matthau’), movie-specific words (‘captures’ and ‘unwatchable’) and general words (‘supposed’ and ‘friendship’). Mentioning a movie or an actor may be a signal whether the review is positive or negative, but they are mostly irrelevant in another domain. Therefore, we want to study the impact of curating the automatically mined keywords on the accuracy of dataless classification models. In Table 11, we show the result with both the original list of keywords in Table 10 and the curated keywords after removing all the underlined domain-specific and noisy keywords. As we expected, there is no improvement on the IMDB test set after curating the keywords. In the Yelp data set, GE and WeSTClass’s accuracy improved while the other models’ accuracy either remained the same or decreased. No conclusion can be drawn but we believe in a real-world application setting, it is worthwhile curating the keywords, especially when the original list of keywords is noisy.