Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines

Abstract

Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin Machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adapt this description to measure how much a word contributes to making documents novel. Our experimental results demonstrate how our approach breaks down novelty into interpretable phrases, successfully measuring novelty.

1 Introduction

The fundamental principle underlying machine learning classifiers is a generalization – the ability to form a decision boundary that differentiates new input into known classes. When training a supervised classifier, it is common to assume that the classes to be recognized are present both in the training and test data [49]. However, given an open world, training on all conceivable classes of input is impractical. This problem introduces the need for novelty detection – the task of spotting input classes that one has not seen before. The problem is particularly severe in text-based supervised classification due to the many-faceted nature of natural language, which gives rise to multiple application-dependent interpretations. Indeed, researchers have for a long time tried to address novelty detection in natural language. So far, no single best model has appeared. Indeed, the success of each model relies on the properties of each particular dataset.

The problem of novelty detection arises in many tasks, such as fault detection [16] and handwritten alphabet recognition [54]. In general, one applies novelty detection when it is required to know whether a given input is similar to or significantly different from the training data. For natural language text, the novelty detector should discern that a text does not belong to a predefined set of topics. Several challenges make such novelty detection particularly difficult:

• 1. Textual information tends to be diverse, composed of large vocabularies.

• 2. Language and topics are typically evolving, making the novelty detection problem dynamic [21].

Lately, the aforementioned challenges have manifested when using supervised learning to build chatbots, an application area that is gaining traction. A chatbot typically needs to handle the language of a multitude of users with evolving information requirements. As such, it must be able to determine when it is capable of answering a query and when it faces a new topic.

Majority of the existing literature on text-based novelty detection addresses one of the following granularity levels:

• 1. Event-level techniques [4] perform topic detection and tracking on a stream of documents.

• 2. Document-level techniques [17] classify an incoming document as known or novel based on its content.

• 3. Sentence-level techniques [6] look for novel sentences within a particular document.

Usually, the sentences/documents are ranked based on some sort of similarity score, obtained from comparing them with previously seen sentences/documents. For instance, the Maximal Marginal Relevance model (MMR) proposed in [14] assigns low scores to previously seen sentences/documents, while assigning high scores to novel ones.

Figure 1 illustrates the problem of novelty detection, contrasting it against anomaly and outlier detection. Anomaly detection [15] concerns discovering anomalies, which are invalid data points. Outlier detection [3, 29], on the other hand, flags legitimate data points that deviate significantly from the mean. Finally, novelty detection [43] is the discovery of entirely new types of data points.

Fig. 1

Visualization of outlier detection, anomaly detection and novelty detection

In contrast to prior work, we here focus on novelty detection at the word level. To this end, we propose a new interpretable machine learning approach for calculating novelty scores for the words within a sentence. The calculation is based on the linguistic patterns captured by a Tsetlin Machine (TM) in the form of AND-rules (i.e., conjunctive clauses). To the best of our knowledge, this is the first study of its kind on this problem.

Problem definition

In the supervised classification setting, i pre-labeled data points D = {(v₁,y₁),(v₂,y₂),…, (v_i,y_i)} is used for training. Here, v_i is the i^th input example and y_i is its class. The input v_i is an t-dimensional real-valued vector $(x_{1}, x_{2}, \ldots , x_{o}) \in \mathbb {R}^{t}$, where x_o refers to the o^th element of the vector. The class y_i ∈ Y = {1,2,…,C_l}, in turn, is an integer class index referring to one out of C_l classes. Learning a classifier entails constructing a classification function f(v;D), $f: \mathbb {R}^{t} \rightarrow Y$, based on the data D. The function simply assigns a label y to the data point v. Our emphasis is novelty scoring, which can be seen as another function $z(v;D), z: \mathbb {R}^{t} \rightarrow \mathbb {R}$. The function computes a real-valued novelty score for input data point v, with the purpose of discerning new classes not found in Y. In this way, a classifier can return the correct class label while flagging novel examples. Considering each element in v to represent a specific word, this paper further extend the novelty detection by introducing a method for breaking down the overall score z(v;D) for v into the contribution of each element x_o. By doing so, we break down novelty into interpretable phrases.

Paper contributions

In this paper, we use the TM to construct conjunctive clauses in propositional logic. In this manner, we capture frequent patterns in the data D, which we then utilize to characterize the known classes Y comprehensively. The novelty score is then calculated based on examining the clauses that match the given input. By further looking into the composition of each clause, we are able to break down the novelty score into the contribution of the different phrases. This decomposition is based on training clauses for the novel data and then measuring the relative frequency of each word inside the clauses for the known classes, contrasted against the relative frequency obtained from the clauses of the novel class. These scores can, in turn, be adopted as input features to machine learning classifiers for novelty detection. Similarly, contextual scores can be calculated simply by inspecting each word’s clauses, providing a local perspective for both novel and known classes.

The remainder of the paper is organized as follows. In Section 2, we first summarize related work before we present the details of the TM in Section 3. This forms the basis for our novelty description architecture, covered in Section 4. In Section 5, we present our empirical results, concluding the work in the last section.

2 Related work

Several studies have been carried out on supervised multiclass classification in a closed-world setting [5]. There is a dearth of work addressing open-world settings [33], with distance-based methods being one of the earliest approaches [28]. These approaches rely on nearest neighbor search, which introduces scalability issues when dealing with larger datasets. Another class of methods are based on single-class classifiers. These include One-Class SVM [50] and SVDD [55]. Further, the decision score from SVM has been used to produce a probability distribution for novelty detection [44]. As no negative training samples are used, single-class classifiers struggle with maximizing the class margin. To overcome the problem of One-Class SVMs, a new learning method named center-based similarity space (CBS) was proposed in [20], which transforms each document in a closed boundary to a central similarity vector that can be used in a binary classifier.

Probabilistic methods have also been utilized for novelty detection [43]. In [30], a technique to threshold the entropy of the estimated class probability distribution is proposed. In that method, choosing the entropy threshold needs prior knowledge. Additionally, the class probability distribution can be misleading when novel data points fall far from the decision boundary. In [32] and [46], an active learning model is proposed to both discover and classify novel classes during training. However, the appearance of novel instances during testing is not considered.

DNNs have recently been used to address the problem of novelty detection. In [61], a two-class SVM classifier is adopted to categorize known and novel classes. An adversarial sample generation (ASG) framework [23] is used to generate positive and negative samples. Similarly, [37] employs generative adversarial networks (GANs), where the generator produces a mixture of known and novel data. The generator is trained with so-called feature matching loss, and the discriminator performs simultaneous classification and novelty detection. In computer vision, the problem of novel image detection is addressed by introducing the concept of open space risk [49]. This is achieved by reducing the half-space of a binary SVM classifier with two parallel hyperplanes that bound the positive region. Although the binary SVM reduces the positive region to half-spaces, their open space risk is still infinite. In [5], a method called OpenMAX is proposed, which estimates the probability of an input belonging to a novel class. In general, the major weaknesses of these methods are high computational complexity and uninterpretable inference. A state-of-the-art GAN-based method for unsupervised outlier detection called Single-Objective Generative Adversarial Active Learning (SO-GAAL) and Multi-Objective Generative Adversarial Active Learning (MO-GAAL) was proposed in [41]. The method is based on a min-max game between a generator and a discriminator. The training process of the generator is paused before convergence to synthesize outliers, which is subsequently used to train the discriminator to recognize the outliers. However, the method is primarily designed for high-dimensional data, requiring extensive problem-specific hyperparameter tweaking. The unsupervised learning method COPOD [40] is a more recent approach that is inspired by copulas for modeling multivariate data distributions. In comparison to other methods, COPOD is computationally efficient, interpretable, and is unaffected by feature dimension. However, the method fails to handle complex features and intricate nonlinear relations.

Apart from the studies on the document-level novelty detection, novelty detection at the event level arises from topic detection, which focuses on the online event and story detection [38]. The study at the event level primarily consists of clustering algorithms that measure the closeness of incoming events or stories to one of the clusters depending on a pre-defined threshold. Novelty detection at the sentence level was investigated in Text Retrieval Conferences (TREC) by highlighting sentences that include novel information given a topic and a list of documents [52]. Based on TREC, many studies have been conducted on novelty detection at sentence level [56, 63], including term translations, Principal Component Analysis (PCA) vectors, Support Vector Machine (SVM) classification, named entities patterns, etc. Likewise, a few approaches have been introduced for learning sentence embeddings, including SkipThought [36], Conceptual Sentence Embedding [58], and FastSent [31]. However, these approaches on embeddings are very dependent on the domain-specific downstream tasks. Recently introduced powerful language models, such as ELMo [42] and BERT [18], have been successful for transfer learning and they are able to learn dynamic sentence embedding in an unsupervised manner.

In [22], a unified attention architecture is proposed to deal with vector representations of text input in NLP. The authors investigate how information can be retrieved from attention in NLP. Further, [51] checks whether the attention weights provide any interpretability by manipulating the weights in pretrained text classification models. They used an intermediate representation erasure method to demonstrate that attention weights are unreliable predictors of the relative significance of the specific input. They thus do not accurately explain the model’s decision-making. Additionally, [53] employed a novel approach for visualizing the attention score for each token. This is the first study on interpretability analysis by visualizing and scoring at the word level. However, as explained in [34], the scoring acquired using attention methods does not provide a meaningful explanation. A more advanced scoring method known as Masked Language Model (MLM) [48] uses pretrained MLM to score sentences using pseudo-log-likelihood scores (PLLs), which involves masking each token one by one. The method becomes unsuitable for scoring the entire tokens of the dataset as the computational complexity rises. Likewise, recent keyword extraction (KE) algorithms such as YAKE [13] and KeyBERT [26] are also used to extract the top-scoring tokens from the trained model. To the best of our knowledge, in novelty detection, there exists no such method to measure each word’s contribution to the novelty. In this study, we expand the study on novelty detection with a method for scoring each word’s contribution to the overall novelty, which offers a clear view to the researchers for the reasoning and the interpretation of the results that the algorithm offers.

3 Tsetlin machine (TM) architecture

The TM, proposed in [24], is a recent approach to pattern classification, regression, and novelty detection [1, 8, 25]. It captures the frequent patterns of the learning problem using conjunctive clauses in propositional logic. Each clause is a conjunction of literals, where a literal is a propositional/Boolean variable or its negation. Recent research reports that the TM performs competitively with state-of-the-art deep learning networks in text classification [7, 47, 59, 60] along with parallel and asynchronous architecture [2] for faster learning across diverse tasks. Further, theoretical studies have uncovered robust convergence properties [35, 62].

A basic TM accepts a vector $X= (x_{1}, \ldots , x_{o}) \in \{0,1\}^{o}$ of o Boolean features as input. For text input, it is typical to booleanize the text to form a Boolean set of words, as suggested in [7]. The input features, together with their negated counterparts, $\bar {x} = \lnot x = 1-x$, form a literal set L = {x₁,…,x_o,¬x₁,…,¬x_o}. For classification problems, the sub-patterns associated with the classes are captured by the TM using m conjunctive clauses $C_{j}^{+}$ or $C_{j}^{-}$. The j = 1,…,m/2 subscript denotes the clause index, while the superscript indicates the polarity of a clause. In brief, half of the clauses are assigned positive polarity, i.e., $C_{j}^{+}$, and the other half are assigned negative polarity, i.e., $C_{j}^{-}$. The positive polarity clauses vote for the input belonging to the class favored by the TM, while the negative polarity clauses vote against that class, that is, for other classes.

A clause $C_{j}^{\xi }, \xi \in \{-,+\},$ is formed by ANDing a subset $L_{j}^{\xi } \subseteq L$ of the literal set. That is, the set of literals for clause $C_{j}^{\xi }$ with polarity ξ can be written as:

$$ C_{j}^{\xi} (X)=\bigwedge_{l \in L_{j}^{\xi}} l = \prod\limits_{l \in L_{j}^{\xi}} l. $$

(1)

The clause evaluates to 1 if and only if all the literals of the clause also evaluate to 1. For example, the clause $C_{j}^{\xi }(X) = x_{1} x_{2}$ consists of the literals $L_{j}^{\xi } = \{x_{1}, x_{2}\}$ and outputs 1, if x₁ = x₂ = 1. The final classification decision is obtained by subtracting the negative votes from the positive votes, and then thresholding the resulting sum using the unit step function u:

$$ \hat{y} = u\left( \sum\limits_{j=1}^{m/2} C_{j}^{+}(X) - \sum\limits_{j=1}^{m/2} C_{j}^{-}(X)\right). $$

(2)

For example, the classifier $\hat {y} = u(x_{1} \bar {x}_{2} + \bar {x}_{1} x_{2} - x_{1} x_{2} - \bar {x}_{1} \bar {x}_{2})$ captures the XOR-relation.

For learning, the TM employs a team of Tsetlin Automata (TA), one TA per literal l ∈ L. Each TA performs one of two actions: either include or exclude its designated literal. Each clause statistically forwards the feedback to its individual TA. The TM employs Type I and Type II feedback. These feedback types control the reward, penalty or inaction received by TAs depending on six factors: (1) target output (y = 0 or y = 1), (2) clause polarity, (3) clause output (C_j = 0 or 1), (4) literals value (x = 1, or ¬x = 1), (5) vote sum, and (6) the current state of the TA. Type I feedback is designed to produce frequent patterns, while Type II feedback increases the discriminating power of the patterns (see [25] for details). The feedback guides the complete system of TAs towards a Nash equilibrium. At any point in the training process, we have m conjunctive clauses per class, half of them positive and half of them negative. These can be retrieved and deployed upon completion of training.

4 Novelty description

By novelty description, we mean the task of characterizing novel textual content at the word level. For instance, the known content may be reviews of mobile phones, while the novel content could be reviews of grocery stores. For this example, one may define the novel content using words associated with grocery stores. However, describing novelty at the word level is nontrivial because the meaning of words varies depending on the context they appear in. For example, consider the word “bat”. This word typically manifests in two distinct contexts- it can denote either “animal” or “sports”. Likewise, the word “bank” can refer to “river bank” or “cash bank”. That is, when contextual meaning is considered, the novelty of the word “bat” and “bank” can be different based on their respective uses. As a result, measuring and describing novel content is a challenging problem.

In general, one can detect and characterize novel content by contrasting against the probability of observing textual content X, given that the content is known. We denote this probability distribution by p_known(X). Assume that the corresponding probability distribution p_novel(X) for novel content also is available. Then, the optimal novelty detection test for a given false positive rate (α) can be obtained by thresholding the likelihood ratio p_novel(X) / p_known(X) [39].

Since neither p_known(X) or p_novel(X) are available to us, we must estimate them using training examples. Inspired by the work in [9] on Semi-Supervised Novelty Detection (SSND), we use two sets of examples. One set represents known content, while the other represents novel content. We obtain these sets by employing a binary classifier that can distinguish between known and novel content, such as the one we proposed in [8].

4.1 Identifying novel word candidates

In our approach, we begin by training a TM on input texts represented as Boolean bag-of-words, i.e., as word sets. A propositional variable represents each word in the vocabulary, capturing the presence/absence of the corresponding word in the input text. We group the texts into two classes, Known and Novel. The first represents known content, and the second represents novel content. Our task is to describe how the second group of text is novel at the word level. To this end, we begin by identifying novel word candidates, followed by scoring and ranking the words based on their contribution to novelty.

Figure 2 shows our architecture for identifying novel word candidates. As seen, upon training, we obtain the clauses of the two classes, Known and Novel. We extract all the words included in the clauses for each class. Each clause contains a combination of both plain ($\mathcal {P}_{L}$) and negated ($\mathcal {N}_{L}$) words. As such, the plain and the negated words serve two different roles. The plain words characterize the corresponding class, while the negated words characterize the other class. We exploit this property as follows, building two bag-of-words (BOW). The first is a bag of known words, referred to as ${\mathscr{B}}_{\mathcal {K}}$, and the second is a bag of novel words, referred to as ${\mathscr{B}}_{N}$.

Fig. 2

Tsetlin Machine architecture for generating word sequences

For class Known, we perform the following procedure:

• We consider the words included in positive clauses first. Here, the plain words $\mathcal {P}_{L}$ are added to the bag of known words ${\mathscr{B}}_{K}$, while the negated words are placed in the bag of novel words ${\mathscr{B}}_{N}$.

• For negative clauses, we do the opposite. The plain words $\mathcal {P}_{L}$ are added to the novel words bag ${\mathscr{B}}_{N}$. The negated words $\mathcal {N}_{L}$, on the other hand, are added to the known word bag ${\mathscr{B}}_{K}$.

The above procedure is inverted for class Novel:

• For the positive clauses, the plain words $\mathcal {P}_{L}$ are added to the novel word bag ${\mathscr{B}}_{N}$, while the negated words are added to the known word bag ${\mathscr{B}}_{K}$.

• Conversely, for the negative clauses, the plain words are added to ${\mathscr{B}}_{K}$, characterizing the known class, while the negated words $\mathcal {N}_{L}$ are added to ${\mathscr{B}}_{N}$.

4.2 Scoring word novelty

With the word bags ${\mathscr{B}}_{K}$ and ${\mathscr{B}}_{N}$ available, we calculate novelty scores at the word level as follows. From the unique words in the bags ${\mathscr{B}}_{K}$ and ${\mathscr{B}}_{N}$, we produce two corresponding word sets, $\mathcal {S}_{K}$ and $\mathcal {S}_{N}$. Assume these respectively contain K and N unique words:

$$ \begin{array}{@{}rcl@{}} \mathcal{S}_{K} &=& \{s_{1}, s_{2},\ldots, s_{k},\ldots, s_{K}\}, \\ \mathcal{S}_{N} &=& \{s_{1}, s_{2}, \ldots, s_{n},\ldots, s_{N}\}. \end{array} $$

(3)

Here, s_k represents a specific word in the set $\mathcal {S}_{K}$, while s_n represents a specific word in the set $\mathcal {S}_{N}$.

We next estimate the occurrence probability $p_{s_{i}}$ of each word s_i in $\mathcal {S}_{K}$, from the known class. The estimate is based on the relative frequency of s_i in the word bag ${\mathscr{B}}_{K}$ as given by (4):

$$ p_{s_{i}}^{\mathcal{K}} = \frac{\mathcal{F}_{i}^{\mathcal{K}}}{{\sum}_{k=1}^{K} \mathcal{F}_{k}^{\mathcal{K}}}. $$

(4)

Here, $\mathcal {F}_{i}^{\mathcal {K}}$ is the frequency of word s_i in ${\mathscr{B}}_{K}$, i.e., the number of times that word s_i has the appropriate role in one of the clauses (as defined in the previous section). To prevent infinite or zero scores, we assume that every word has a minimum frequency of 1. In the following, we denote the set of relative frequencies for the words from ${\mathscr{B}}_{K}$ by $p_{\mathcal {K}}$, while $p_{\mathcal {N}}$ is the set of relative frequencies for the words from ${\mathscr{B}}_{N}$, as captured by (5):

$$ \begin{array}{@{}rcl@{}} p_{\mathcal{K}}&=& \{p_{s_{1}}^{\mathcal{K}}, p_{s_{2}}^{\mathcal{K}}, \ldots, P_{s_{K}}^{\mathcal{K}}\},\\ p_{\mathcal{N}}&=& \{p_{s_{1}}^{\mathcal{N}}, p_{s_{2}}^{\mathcal{N}},\ldots, p_{s_{N}}^{\mathcal{N}}\}. \end{array} $$

(5)

The calculation of the novelty score for each word depends on whether $s_{i} \in \mathcal {S}_{K}$, $s_{i} \in \mathcal {S}_{N}$, or both, as shown in (6):

$$ \mathit{Score}(s_{i}) =\begin{cases} \frac{p_{s_{i}}^{\mathcal{N}}}{p_{s_{i}}^{\mathcal{K}}}&\text{if } s_{k} \in \mathcal{S}_{K} \cap \mathcal{S}_{N},\\ 0&\text{if } s_{k} \in \mathcal{S}_{K} \setminus \mathcal{S}_{N},\\ \infty&\text{if } s_{k} \in \mathcal{S}_{N} \setminus \mathcal{S}_{K}. \end{cases} $$

(6)

Here, $p_{s_{i}}^{\mathcal {N}}$ and $p_{s_{i}}^{\mathcal {K}}$ denote the estimated occurrence probabilities of the word s_i from $p_{\mathcal {N}}$ and $p_{\mathcal {K}}$, respectively. The score defines how much a word contributes in a sentence/document to make it novel. That is, a higher score signals higher novelty and vice versa. Figure 3 shows the resulting TM-based architecture and flow of information for the above scoring approach.

Fig. 3

Novelty scoring calculation for each word

Additionally, we also propose a contextual scoring approach to capture multiple word meanings determined by context. We presume that words that appear in the same clause are related semantically, and accordingly, we use clause co-occurrence of words to measure semantic relations. The intent is to differentiate between, for example, the meaning of “apple” in “apple phone” and the meaning of “apple” in “apple fruit”. We achieve this through leveraging clauses that capture “apple” and “phone” in combination with other clauses that capture “apple” and “fruit”.

The scoring is again performed in two steps:

• 1. Rather than measuring the frequency of individual words, we now measure frequency of co-occurrence among the TM clauses. For instance, let us consider the word pair (s₁,s₂) and novel class, associated with a total number of m clauses. The frequency of the word pair occurring together in the clauses is then given as:

  $$ p_{s_{1}, s_{2}}^{\mathcal{N}}=\frac{\mathcal{F}_{s_{1},s_{2}}^{\mathcal{N}}}{m}. $$

  (7)

  Here, $F_{s_{1}, s_{2}}^{\mathcal {N}}$ is the number of times the word pair occur together across the m clauses of the novel class.

• 2. Finally, the contextual score for the word pair (s₁,s₂) in class Novel can be defined as:

  $$ \mathit{Score}_{\mathit{context}}^{\mathcal{N}}(s_{1}, s_{2}) = \frac{p_{s_{1}, s_{2}}^{\mathcal{N}}}{p_{s_{1}}^{\mathcal{N}} \times p_{s_{2}}^{\mathcal{N}}}. $$

  (8)

  Above, $p_{s_{2}}^{\mathcal {N}}$ and $p_{s_{1}}^{\mathcal {N}}$ are the individual frequencies of each word across the novel clauses, from the previous subsection.

Notice how the above score increases with lower individual frequencies and higher joint frequency, measuring dependence over the clauses. In the same way, we can calculate dependence over the clauses for the known class as well.

4.3 Case study

We now demonstrate our novelty description approach, steb-by-step, using two example sentences from the sports domain. For illustration purposes, we consider the class Cricket to be Known and the class Rugby to be Novel.

• Class : Cricket (Known) Text: England won the cricket match by hitting six in the last ball. Words: “England”, “won”, “cricket”, “match”, “hit”, “six”, “ball”.

• Class: Rugby (Novel) Text: England won the rugby match despite using old ball. Words: “England”, “won”, “rugby”, “match”, “despite”, “old”, “ball”.

We first create the set of 10 unique words W = {“England”, “won”, “cricket”, “match”, “hit”, “six”, “ball”, “rugby”, “despite”, “old”} from the words in the two sentences, each with a unique index o. From this set, we produce the input feature vector for the TM, X = [x₁,x₂,…,x₁₀]. Each propositional input x_o in X refers to a particular word. Jointly, the propositional inputs are used to represent an input text. If a word w_o ∈ W is present in the document, the corresponding propositional input x_o is set to 1, otherwise, it is set to 0.

After TM training, we obtain a set of clauses, as examplified in Table 1. The clauses $(C_{1}^{+})_{\mathcal {K}}$, $(C_{2}^{+})_{\mathcal {K}}$, $(C_{1}^{-})_{\mathcal {N}}$, $(C_{2}^{-})_{\mathcal {N}}$ vote for class Known, while $(C_{1}^{-})_{\mathcal {K}},~(C_{2}^{-})_{\mathcal {K}}$, $(C_{1}^{+})_{\mathcal {N}}, (C_{2}^{+})_{\mathcal {N}}$ vote for class Novel. These clauses are then used to produce two bag-of-words, ${\mathscr{B}}^{K}$ and ${\mathscr{B}}^{N}$. All the plain words in $(C_{1}^{+})_{\mathcal {K}}$, $(C_{2}^{+})_{\mathcal {K}}$, $(C_{1}^{-})_{\mathcal {N}}$, $(C_{2}^{-})_{\mathcal {N}}$ are placed in ${\mathscr{B}}^{K}$, while all the negated words are placed in ${\mathscr{B}}^{N}$. Since none of the words are negated in the clauses, we now have ${\mathscr{B}}^{K}$ = (“England”, “cricket”, “match”, “hit”, “six”, “cricket”, “six”, “cricket”, “won”, “six”, “ball”, “cricket”, “hit”, “six”). Correspondingly, all the plain words in $(C_{1}^{-})_{\mathcal {K}},~(C_{2}^{-})_{\mathcal {K}},~(C_{1}^{+})_{\mathcal {N}}, (C_{2}^{+})_{\mathcal {N}}$ are placed in ${\mathscr{B}}^{N}$, while all the negated words are placed in ${\mathscr{B}}^{K}$.

Table 1 Clauses with conjunctive word patterns for known and novel class

Within each bag-of-words, each word occurs with a certain frequency. For instance, the word “match” occurs once in ${\mathscr{B}}^{K}$ and twice in ${\mathscr{B}}^{N}$. Notice that the total number of word occurrences are different for each class – 14 words in class Known and 13 words in class Novel. Hence, the relative frequency for “match” in class Known becomes $p^{\mathcal {K}}_{\mathit {match}} = \frac {1}{14} = 0.071$ while for class Novel it becomes $p^{\mathcal {N}}_{\mathit {match}} = \frac {2}{13} = 0.154$. Table 2 lists the frequencies of the words per class.

Table 2 Relative frequency and score for each word

We are now ready to calculate the novelty score for each word in W. Let us consider the word “rugby” from the novel word set and the word “cricket” from the known word set. For “rugby”, we first calculate its relative frequency (4). In the bag-of-word ${\mathscr{B}}_{N}$ for class Novel, “rugby” occurs four times, i.e., $\mathcal {F}^{\mathcal {N}}_{\mathit {rugby}} = 4$. Since we assume that a word has a minimum frequency of 1, we further have $\mathcal {F}^{\mathcal {K}}_{\mathit {rugby}} = 1$, despite “rugby” not appearing in the text from class Known.

From Table 2, we observe that the total word frequencies for the known and novel classes are 14 and 13, respectively. Hence, the relative frequencies for “rugby” becomes $p_{rugby}(\mathcal {K}) = 0.307$ for class Known and $p_{rugby}(\mathcal {N})= 0.071$ for class Novel (4).

Because the clauses characterize each class Known and Novel, notice how “rugby” gets the relatively high novelty score Score_rugby = 4.651. That is, its relative frequency is high in the novel class and low in the known class. Conversely, the word “cricket” is repeated four times in ${\mathscr{B}}^{K}$ and once in ${\mathscr{B}}^{N}$. Its relative frequencies thus becomes $p_{\mathit {cricket}}(\mathcal {K}) = 0.28$ for class Known and $p_{\mathit {cricket}}(\mathcal {N})= 0.076$ for class Novel. Accordingly, the novelty score becomes Score_cricket = 0.271, which is a low score denoting a strong inclination of the word towards the known class.

Overall, Table 2 shows how the words characterizing class Known get a relatively low novelty score, while those characterizing class Novel obtain high scores.

5 Results and discussions

In this section, we evaluate our proposed novelty description approach on two publicly available datasets: BBC Sports and Twenty Newsgroups. The performance of the TM framework for novelty detection was previously investigated in [8] and is summarized in Table 3. Notably, as has been found across several datasets, a one-class SVM on the simple mean embeddings established a strong baseline. Here, we further explore our model’s effectiveness at producing discriminative novelty scores at the word level using TM clauses. To obtain robust performance and ensure that the results are not influenced by the data, we perform a one-class classification using leave-one-out evaluation on 20 Newsgroup dataset. This paper deals with the post-processing after novelty detection to deal with the novelty scoring at the word level. However, the leave-one-out evaluation is necessary because this study leverage the performance of the TM framework in terms of novelty detection. We employ the ROC AUC to quantify the novelty detection performance by using the ground truth labels during testing. Table 4 shows the performance comparison of our method and the baseline algorithms, including a one-class classifier. In the leave-one-out setup, one of the classes is considered a known class, while the remaining classes are treated as novel. The training is conducted using a known class, whereas testing is carried out on samples from a novel class. The ROC AUC is computed during testing with the assumption that the samples from the known class are labeled as y = 0 and from novel class as y = 1. Our method outperforms baselines algorithms in five out of six evaluation setups with a significant margin.

Table 3 Performance comparison of TM framework with cluster and outlier-based novelty detection algorithms

Table 4 ROC AUC (%) of one-class classification with leave-one-out evaluation on 20 Newsgroup

In the following, we compare the scoring mechanism of our framework with attention and TF-IDF as a baseline. To ensure a fair comparison, the attention score for each word is calculated as described in Section 5.1.1. For TF-IDF, We calculate TF separately for the known and novel classes. Conversely, IDF is calculated using all the documents from both classes (to suppress common words such as stop words). Unlike attention and TF-IDF, even if a word is present in most documents, our scoring considers both relevance and context. For example, if a word from class Novel also is present in class Known, our model can nevertheless assign more weight to that word. This happens when a word, while syntactically the same in both classes, acquires a novel meaning in the novel class due to its appearance in a novel context. The latter contextual information is captured through those clauses of the novel class that trigger for that word. As such, attention and TF-IDF are not context-aware. Moreover, these methods prove especially beneficial on more extensive datasets, such as 20 Newsgroups and BBC Sports, since they filter out general language contexts that are less discriminative for the characterization of a text corpus, making them a strong baseline for performance comparison.

To provide a comparison, we plot the cumulative frequency distribution (CFD) for the scores of (1) the words only found in the novel dataset, (2) the words only found in the known dataset, and (3) the words shared by both datasets. In brief, the CFD demonstrates that the word scores generated by the baseline are relatively similar for both known and novel classes. Thus, the baseline methods lack the discriminatory power necessary to distinguish between the two categories of words.

5.1 Baseline

5.1.1 Attention mechanism

We utilize the weights from attention’s layers input representation $\mathcal {A}$ of the trained model. The importance of each token is calculated based on the attention it receives. For instance, if attention to the token $c \in \mathcal {A}$ is higher than the token $d \in \mathcal {A}$, then c is assumed to be “more significant” than d to the model’s output. In our work, the scores are calculated using scaled-dot product attention mechanism [57].

Let us consider an input sequence of length o, X = (x₁,x₂,…,x_o), where x_i represents the i^th token whose representation in the attention layer is $h_{i} \in \mathbb {R}^{t}$. The attention score for the i^th token is as follows:

$$ \alpha_{i} = \frac{h_{i} \times V}{\beta}, $$

(9)

where the parameter β is the scaling factor, and $V \in \mathbb {R}^{t}$ is the context vector that can be seen as a fixed query requesting the “most important token” from input. Either the word embedding or the encoder’s output can denote token representation h_i. The attention weight can be expressed as:

$$ a_{i} = \frac{exp(a_{i})}{{\sum}_{i^{\prime}} exp(a_{i^{\prime}})}. $$

(10)

Finally, the complete input sequence is denoted as:

$$ h = \sum\limits_{i} (a_{i} h_{i}). $$

(11)

In our experiment, we retrieve the attention score and weights for each token using (9) and (10) respectively.

We conducted experiments using scaled dot-product attention (DP) and additive attention with varying scaling factors (β). The attention scores in our experiments are generated using a Long short-term memory (LSTM) with DP and an affine transformation layer as the input encoder. We used the Adagrad optimizer [19] for gradient descent and used dropout as regularization to prevent over-fitting. To eliminate the influence of prior knowledge, we learn all parameters from scratch and initialize the pre-trained word embeddings with a uniform distribution and dimension d = 100. A softmax function is applied over a linear layer for obtaining the final classification output. The readers are referred to [53] for a detailed theoretical explanation to generate the attention scores.

5.1.2 Term frequency-inverse document frequency (TF-IDF)

A commonly used method to analyze the importance of a word is the term frequency-inverse document frequency (TF-IDF) [45]. TF-IDF weighs each word to statistically measure the significance of the word in a given document. To this end, TF-IDF consists of two factors: normalized term frequency (TF) and inverse document frequency (IDF). TF measures the frequency of the word in the document, whereas IDF measures the uniqueness of the word across documents:

$$ TF-IDF_{s} = \frac{\mathcal{F}_{s}}{\mathcal{F}} \times \log_{2} \frac{|D|}{|D_{s}| + 1}. $$

(12)

Here, $\mathcal {F}_{s}$ is the frequency of the word s in the target document, $\mathcal {F}$ is the sum of the target document word frequencies, |D| is the total number of documents, and |D_s| is the number of documents containing the word s.

5.1.3 Keyword extraction algorithms

Our method extracts keywords from known and novel classes based on the novelty scores. As a result, we also compare the significant words obtained by our method to those captured by existing keyword extraction (KE) algorithms. To do this, we first separate the text documents from known and novel classes before passing them to the KE algorithms. Additionally, we present the top 10 keywords captured by these algorithms. For the KE baselines mentioned below, we use the pke package [10]:

• TopicRank [12]: This is a graph-based KE method that depends on the extraction of the top-ranked topic.

• YAKE [13]: A lightweight statistical approach for KE.

• MultipartiteRank [11]: An unsupervised KE method for encoding topical information in a multipartite graph structure.

• BERT-MMR [26]: A KE method that leverages Bidirectional Encoder Representations from Transformers (BERT) embeddings and Maximal Marginal Relevance (MMR).

5.2 Evaluation measures

We use the accuracy, Receiver Operating Characteristics (ROC) curve, precision, and recall to evaluate the performance of novelty detection using word scores obtained from the proposed method. In general, accuracy is a well-known parameter to measure the effectiveness of novelty detection models, which indicates the percentage of correct prediction by a model in a test set. The accuracy is calculated by:

$$ Accuracy= \frac{T_{P} + T_{N}}{T_{P} + T_{N} + F_{P} + F_{N}}, $$

(13)

where T_P, T_N, F_P, F_N denotes the samples that are correct novel, correct normal, incorrect novel, and incorrect normal respectively. And P, N denotes the total novel and normal samples. The precision is defined by the percentage of correctly identified novel samples and is give as:

$$ Precision = \frac{T_{P}}{T_{P} + F_{P}}. $$

(14)

Recall is the percentage of the real novel samples identified and is given as:

$$ Recall = \frac{T_{P}}{T_{P} + F_{N}}. $$

(15)

In general, the higher the precision and the recall, the better the algorithm. However, the precision and recall are mutually constrained. For example, if only one novel sample is detected, the precision is 100%, while the recall is very low. And if all samples detected are novel, the recall will be 100%, while precision tends to be very low. Therefore, we present precision-recall graph in our evaluation.

The ROC is insensitive to the number of novel samples and is calculated by plotting all potential choices of the T_P rate (the portion of novel data ranked among the total novel data) against the F_P rate (the portion of normal data ranked among the total novel data). The ROC curve can be summarized using a single value defined as the area under ROC curve (AUC). The ROC value ranges between 0 and 1 and is regarded as average of the recall. The perfect detection of all test samples would result in ROC value of 1, whereas the null detection would result in ROC value of 0. In general, the greater the ROC AUC value, the better the algorithm. [27] established that the ROC AUC value corresponds to the probability of a pair (nov, nor), where nov is certain true novel samples and nor is certain true normal samples. The ROC AUC can then be defined by:

$$ ROC~AUC = \left\{ \begin{array}{cl} 1, & \mathbf{if}~ score(nov) > score(nor), \\ 0, & \mathbf{if}~ score(nov) < score(nor), \\ 1/2, & \mathbf{if}~ score(nov)~=~score(nor). \end{array} \right. $$

Therefore, the ROC AUC has a direct probabilistic interpretation. The AUC can be also be defined as:

$$ AUC = {{\int}_{0}^{1}} ROC(T)~d_{T}, $$

(16)

where T denotes a threshold to control novel samples. The ROC AUC is the most often used evaluation metric for novelty detection that provides a ranking. Therefore, in this paper, we compare the evaluation alongside other methods, so it can give different aspects of the performance. To ensure fairness, effectiveness and reproducibility of the evaluation results, we use scikit-plot library¹ to compute ROC and precision-recall graphs.

5.3 BBC sports dataset

The BBC sports dataset comprises 737 documents from the BBC sport website organized in five sports article categories, collected from 2004 to 2005. The resulting vocabulary encompasses 4 613 terms. For our experiment, we consider the classes “cricket” and “football” to be known and the class “rugby” to be novel, thus creating an unbalanced dataset. For preprocessing, we perform tokenization, stopword removal, and lemmatization. We run the TM for 100 epochs with 10 000 clauses, a voting margin T of 50, and a sensitivity s of 25.0.

We present overall novelty score statistics for the words captured by the clauses in Table 5. The table demonstrates that words in the class Novel have distinctively higher average scores than words in the class Known. Also, notice that the shared words have the highest mean and standard deviation. As analyzed further below, this is the case because the TM will mainly use those words when forming the decision boundary between the two classes. As a result, the shared words will appear in more clauses as characterizing class features. That is, the clauses will either single out the words in one class or suppress the words in the other class.

Table 5 Overall word statistics for BBC sport dataset

To gain further insight into the properties of the novelty score, we plot the CFD for the scores of the novel, known, and shared words in Fig. 4. We further compare these CFDs with the corresponding ones obtained using attention weights in Fig. 5 and TF-IDF in Fig. 10. As can be observed from the plot, our approach produces more distinctive novelty scores than both attention and TF-IDF. The novel words typically produce high scores, while the known words produce low scores. In particular, as shown in Fig. 4a, 85% of the known words output scores lower than 1.0. On the other hand, as seen in Fig. 4b, only approximately 45% of the words unique for the novel class have scores below 1. The majority of the uniquely novel words produce scores greater than 1.

Fig. 4

Cumulative frequency distribution (CFD) graph for word scores in different categories of BBC Sports using TM

Fig. 5

Cumulative frequency distribution (CFD) graph for word scores in different categories of BBC Sports using attention weights

We plot the TM and attention scores for each token in Fig. 6b and a, respectively. Due to the large span of the TM scores, the y-axis is plotted on a log scale. Nonetheless, we note that the scores are structured in successive layers, with known scores at the bottom, novel scores at the top, and shared scores in the center. We notice that even the attention score demonstrates a small degree of differentiation between known and novel categories. However, the variability of the score is quite low when compared to the score generated by TM as seen in Fig. 7 boxplot. Additionally, the shared word scores produced by the attention mechanism exhibit a high degree of resemblance to known word scores.

Fig. 6

Visualization of tokens in known, Novel and Shared categories from BBC Sports

Fig. 7

Boxplot of scores in known, Novel and Shared categories from BBC Sports

Fig. 8

ROC curve and precision-recall of known/novel class classification of BBC Sports using word scores obtained from TM

Finally, we plot the scores for words that are shared between the known and novel classes in Fig. 4c. As can be observed, the words that are shared produce both high and low scores. To cast further light on this observation, we investigate the words that are shared further in Table 6. We see that the words captured frequently by novel clauses have high scores, whereas the words that are frequent in known clauses have low scores. Additionally, common words (e.g., stopwords), also have low scores. For example, the word “Rugby”, which is highly characteristic for class Novel, is repeated only 5 times in the clauses representing class Known. For the clauses that represent class Novel, on the other hand, it is repeated 215 times. In other words, the shared words constitute words that are either characteristic for class Known or class Novel. This finding also suggests that the scores can be calculated accurately even if the words are present in both categories. We analyze the most frequently used words to obtain an intuition of the overall theme captured by the clauses. We generate such lists by counting the top words according to the highest scores from known and novel classes. Such a list may assist a user in weighting and selecting relevant words in a specific application.

Table 6 Composition of shared words in BBC Sport

Tables 7 and 8 show an example of a top word list for each class, from which we make the following observations. First, our proposed method assigns low scores to words belonging to known classes while assigning comparatively high scores to words belonging to the novel class. In general, the words that appear in a novel context are boosted. Second, the words that are most representative for the respective classes are captured frequently by clauses, making them the most repeated ones. Third, the keywords captured by other KE baselines are comparable to those extracted by our method and accurately define the corresponding classes. We observe that TopicRank, YAKE, and MultipartiteRank all yield words with a high degree of similarity to our approach. Additionally, we notice that BERT-MMR exhibits the worst performance. This might be due to the fact that we utilized pre-trained sentence embedding for BERT, and the keywords are extracted from overall documents. Even though the words are not highly relevant to the classes, BERT is capable of producing words relating to the class’s general theme. For example, sports-related words are included in both classes.

Table 7 Example of top words extracted from KE baselines for the Known class in BBC Sports

Table 8 Example of top words extracted from KE baselines for the novel class in BBC Sports

We now investigate the degree of discrimination power our novelty scoring provides, and therefore uniquely describes novelty at the word level. To this end, we employ logistic regression for classifying novel text based on the word scores obtained from our method. The ROC and precision-recall curves of the experiment are depicted in Fig. 8 for our novelty scoring mechanism. Our method provides the competitive ROC value due to its ability to discriminate novel samples based on their scores. This capability enables our method to acquire a higher true positive T_P rate since it makes separate analysis of both correct novel, i.e., true positive T_P and correct normal, i.e., true negative T_N. Figures 9a, 10, and 11 contains corresponding curves when TF-IDF and attention scores are used instead. We see that the classification performance for our novelty scores is substantially better than what is obtained with TF-IDF. Attention score outperforms our approach for BBC Sports dataset by a small percentage. However, our approach outperforms attention in 20 Newsgroups. This can be attributed to the capability of our approach to deal with a big dataset.

Fig. 9

ROC curve and precision-recall of known/novel class classification of BBC Sports using attention scores

Fig. 10

Cumulative frequency distribution (CFD) graph for TF-IDF scores in different categories of BBC Sports

Fig. 11

ROC curve and precision-recall of known/novel class classification of BBC Sports using TF-IDF scores

5.4 20 newsgroups dataset

The 20 Newsgroups dataset contains a total of 18 828 documents partitioned equally into 20 separate classes. In our experiments, we treat the two classes “comp.graphics” and “talk.politics.guns” as Known topics, and then use the class “rec.sport.baseball” to represent a Novel topic. Again, we train a TM to produce our clause-based novelty scores. The overall statistics of the resulting word scores are shown in Tables 9 and 10, where we observe similar behavior to that observed with the BBC Sports dataset.

Table 9 Overall word statistics for 20 Newsgroups dataset

Table 10 Composition of shared words in 20 Newsgroups dataset

The CFD plot in Fig. 12 presents the score distribution among words per group (known, novel, shared). For known words, in Fig. 12a, we find that 90% of the scores of the words are below around 1.3. In Fig. 12b, however, only 45% of the novel word scores fall below approx. 1.3. From the plots, it is evident that the majority of the novel words have considerably higher scores than the known words. Note that some of the novel word’s low scores are attributable to the presence of common words (e.g., stop words) in the novel bag-of-words. Since the common words, as such, do not signify novelty, the TM clauses do not frequently capture them. As a result, they receive relatively low scores despite their appearance among the novel documents.

Fig. 12

Cumulative frequency distribution (CFD) graph for word scores in different categories of 20 Newsgroups using TM

The CFD plot for attention and TF-IDF both exhibit similar behaviour to that of BBC Sports, as seen in Fig. 13 and 18, respectively. Finally, we again observe that the clauses have used the shared words for discrimination (cf. Table 10), resulting in a mix of low and high novelty scores, as shown in Fig. 12c.

Fig. 13

Cumulative frequency distribution (CFD) graph for word scores in different categories of 20 Newsgroups using attention weights

Table 11 and Table 12 provide examples of the highest-scoring words captured by KE baselines, including TM, for both classes. The visualization of the scores are presented in Figs. 14 and 15. Again, we observe a similar behavior as for the BBC Sports dataset. The ROC and precision-recall curves for our novelty scoring mechanism are illustrated in Figs. 16a, 17a, 18, and 19a include corresponding graphs when TF-IDF and attention scores are used instead. Our method outperforms the ROC value obtained from attention because of its ability to identify more number of correct novel samples, i.e., true positives TP. However, the TF-IDF surprisingly outperforms both of the methods because of its straightforward scoring system and the dataset’s moderate size. We can see that our scoring approach outperforms the baselines by a wide margin.

Table 11 Example of top words extracted from KE baselines for the known class in 20 Newsgroups

Table 12 Example of top words extracted from KE baselines for the novel class in 20 Newsgroups

Fig. 14

Visualization of tokens in known, Novel and Shared categories from 20Newsgroups

Fig. 15

Boxplot of scores in known, Novel and Shared categories from 20Newsgroups

Fig. 16

ROC curve and precision-recall of known/novel class classification of 20 Newsgroups using word scores obtained from TM

Fig. 17

ROC curve and precision-recall of known/novel class classification of 20 Newsgroups using attention scores

Fig. 18

Cumulative frequency distribution (CFD) graph for TF-IDF scores in different categories of 20 Newsgroups

Fig. 19

ROC curve and precision-recall of known/novel class classification of 20 Newsgroups using TF-IDF scores

5.5 Contextual scoring

We also implement a context-based scoring approach to investigate how multiple words interact to capture novelty. As detailed in Section 4, we compute the combined novelty score by measuring word co-occurrence in clauses. That is, we intend to demonstrate how context can help uncover novelty when words have multiple meanings. The context-based scoring is critical since the context can transform the word from being novel to known, such as the meaning of the word “apple” in “apple fruit” and “apple phone”. For demonstration, we calculate our proposed context-based novelty score for five words (i.e., two known, two novel, and one common word) in both datasets. For the BBC Sports dataset, the pairwise co-occurrence scores are presented in Table 13. We see a significant degree of correspondence between words such as “Manchester” and “Chelsea” from class Known. Similarly, there is a high correspondence between words such as “Rugby” and “Flyhalf” from class Novel. The common word “Particular”, on the other hand, shows similar correspondence with words from both of the classes. Similarly, for the 20 Newsgroups dataset, the co-occurrence scores for five words selected from the known, novel, and common word types are shown in Table 14. The words “Guns” and “Weapon” are from class Known and manifest strong co-occurrence. Additionally the words “Baseball” and “Player” from class Novel correspond strongly as well. The common word “Gather”, on the other hand, co-occurs within both of the classes. These examples demonstrate that the words that are most likely to appear in the same context have a high co-occurrence score. This can be explained by the fact that many clauses capture words that frequently occur together in a similar context.

Table 13 Co-occurrence matrix showing the information gain between words in BBC Sports

Table 14 Co-occurrence matrix showing the information gain between words in 20 Newsgroup

We compare the contextual scores obtained from our method with the Word2Vec similarity score. To do this, we utilize Gensim library to train custom Word2Vec on both datasets. Gensim library enabled us to create word embeddings by training own Word2Vec models on a custom corpus using either CBOW or skip-grams algorithms. Parameter-wise, we used an embedding size of 200 and a window size of 5. We compute the cosine similarity between words by using their word vectors (embeddings). The findings are included in Tables 15 and 16. We notice a significant degree of resemblance between the corresponding words from the known and the novel classes. However, unlike our method, the similarity scores are less distinct, and the common words are not discernible score-wise.

Table 15 Co-occurrence matrix showing the similarity between words in BBC Sports using Word2Vec

Table 16 Co-occurrence matrix showing the similarity between words in 20 Newsgroup using Word2Vec

6 Conclusion

In this work, we propose a Tsetlin Machine (TM)-based solution for word-level novelty description. First, we employ the clauses from a trained TM to capture how the most significant words differentiate a group of novel documents apart from a group of known documents. Then, we calculate the score for each word based on the role it plays in the clauses. The analysis of our empirical results for BBC Sports and 20 Newsgroups demonstrate significantly better novelty discrimination power when compared to using attention and TF-IDF. Our empirical results also show that we can capture word relations through a contextual scoring mechanism that measures co-occurrence within TM clauses. By capturing non-linear relationships among words, we can enhance the capability of measuring novelty at the word level. However, training a TM is computationally more expensive than calculating TF-IDF, particularly for large datasets with an extensive vocabulary. We will address computation speed in our future work, employing indexing mechanisms and exploiting feature space sparsity.