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DEVELOPMENT OF A SOFTWARE PACKAGE FOR PROBLEMATIC TEXT DATA
BASED ON NEURO-FUZZY MODELING
Xudoyqulov Adhamjon Sunnatullo o'g'li
Nukus State Technical University
E-mail:
Davletov Guvanch Atajanovich
Nukus State Technical University
E-mail:
Scientific advisor:
Kuanishbay Kenesbayevich Seitnazarov
,
Doctor of Technical Sciences, Professor.
Abstract:
Textual data from sources such as social media, messaging, and OCR often
contain problematic elements – they are noisy, ambiguous, or incomplete – posing challenges
for traditional natural language processing (NLP) techniques. This paper presents a
hybrid
neuro-fuzzy
modeling approach to robustly process such problematic text data. We
combine fuzzy logic’s strength in handling uncertainty and imprecision with neural networks’
learning capability to create a software system that can normalize noisy text, interpret
ambiguous language, and make reliable decisions even with incomplete information. We
outline the architecture of the proposed system, which integrates fuzzy inference modules
(capturing expert linguistic rules and similarity measures) with trainable neural network
components that adapt these rules to data. Practical examples demonstrate how the system
corrects slang and spelling variations and resolves ambiguities in context.
Keywords:
neuro-fuzzy modeling, fuzzy logic, noisy text processing, text normalization,
natural language processing (nlp), text classification, interpretability.
Introduction
Textual data encountered in real-world applications is often far from clean or well-
structured.
Noisy text data
– characterized by misspellings, typos, slang, and other
irregularities – has emerged as a key challenge for NLP systems. Modern communication media
like SMS, chats, and social networks encourage informal language and brevity, leading to non-
canonical text that deviates from standard grammar and spelling. This noise can dramatically
degrade the performance of language models and classifiers. For example, Bagla et al. (2021)
showed that as little as 5–10% character-level noise (random typos) in input data causes
significant drops in accuracy across tasks like classification, question answering, and named
entity recognition. In one benchmark, injecting 10% typos reduced BERT’s accuracy from
roughly 72% to 55%, underscoring how fragile traditional NLP models can be to noisy text.
Aside from noise, textual data can be
ambiguous
– words or phrases may have multiple
interpretations or unclear references – and often
incomplete
, as in terse user queries or
fragmentary sentences. Human communication frequently omits context or assumes shared
knowledge, making it difficult for rigid algorithms to parse the intended meaning.
Researchers have long recognized these issues. Early studies examined how specific
noise types affect NLP: e.g., Taghva et al. (2000) found that OCR errors impair text
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classification, while Agarwal et al. (2007) and Subramaniam et al. (2009) surveyed the impact
of various noise kinds (spelling errors, missing punctuation, etc.) and techniques to handle them.
Common strategies for noisy text processing include data cleaning (normalization) and
designing robust models. Rule-based text normalization methods can map non-standard words
to their standard forms (e.g., “u” → “you”, “4get” → “forget”). For instance, a recent study
normalized Malay social-media text by combining Levenshtein distance (for spelling correction)
with rule-based slang replacement, achieving about 80% conversion of noisy words to standard
words. However, purely rule-based solutions often struggle to cover the open-ended variety of
noise in user-generated content. On the other hand, purely statistical or neural models, while
data-driven, tend to treat noise as just another variation – which can confuse learning and
degrade accuracy if not explicitly addressed.
In this context,
fuzzy logic
offers an attractive way to handle uncertainty and vagueness
in text. Fuzzy set theory allows an item (e.g., a word or a document) to belong to multiple
categories or have multiple interpretations with graded membership values between 0 and 1.
This is very suitable for language, where a word might partially match a slang dictionary entry,
or a sentence might belong to more than one intent category. Traditional classification assigns
each example to one class (crisp decision), but in practice, overlapping classes are common,
especially in text domain. For example, a forum post might be both a question and a complaint,
or a movie review might be “somewhat positive”. Fuzzy approaches enable representing such
overlaps: a text can belong to several classes with certain degrees of membership.
Bodyanskiy et al. (2012) noted that text annotation tasks often produce “fuzzy situations”
requiring flexible classification beyond crisp yes/no assignment. Fuzzy semantic similarity
measures and fuzzy ontologies have also been explored to better capture partial matches in
meaning, reflecting the inherently gradational nature of human language.
To leverage fuzzy logic in an adaptive, data-driven way, hybrid
neuro-fuzzy
systems
have been proposed in prior work. A neuro-fuzzy system integrates a fuzzy inference
mechanism with neural network training algorithms. The neural component tunes the
parameters of the fuzzy system (such as membership functions and rule weights) based on data,
rather than relying solely on manual rules. This synergy can yield models that handle imprecise
linguistic inputs while still learning from examples. Indeed, neuro-fuzzy models have been
applied to various NLP tasks over the years. Early examples include adaptive neuro-fuzzy
classifiers for document categorization and sentiment analysis. Rustamov and colleagues, for
instance, developed neuro-fuzzy approaches for sentiment analysis and subjectivity detection
around 2012–2013. Aida-Zade et al. (2018) applied an Adaptive Neuro-Fuzzy Inference System
(ANFIS) to sentence-level sentiment classification and call-center intent detection, achieving
accuracy (~92%) on par with or better than classical machine learning methods. More recently,
Vashishtha & Susan (2021) introduced MultiLexANFIS, a neuro-fuzzy network for social
media sentiment analysis that ingests multiple lexicon-based features. Their system could
effectively handle the “fuzziness” of informal language and outperformed not only baseline
fuzzy models but also some deep learning models on tweet sentiment benchmarks. Such results
suggest that neuro-fuzzy hybrids can combine the interpretability and domain knowledge of
fuzzy systems with the pattern-learning strength of neural networks, yielding robust
performance. In general, the field has seen a resurgence of interest in
deep neuro-fuzzy
systems
as a way to build more transparent AI – addressing the “black-box” nature of deep
learning by embedding understandable fuzzy rules. Liu et al. (2024) provide a comprehensive
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survey of efforts to fuse fuzzy theory with NLP, noting both the promise of this fusion and
challenges like scalability and integration with modern large-scale neural architectures.
Our contribution in this paper
is a novel neuro-fuzzy software system tailored for processing
problematic textual data. We focus on texts that are noisy (containing misspellings, informal
abbreviations, etc.), ambiguous (having uncertain or multiple interpretations),
or incomplete (lacking context or grammatical completeness). The system is designed to
perform end-to-end processing: from cleaning and normalizing the text, through to analyzing or
classifying it, within a unified neuro-fuzzy framework. In the following sections, we first
review related work in more detail, then describe the architecture and methodology of our
proposed system. We provide illustrative examples of the system in action on complex text
cases, and we present experimental evaluations on benchmark datasets with injected and real
noise. We then discuss the findings – how the neuro-fuzzy approach improves robustness and
what trade-offs it entails – and outline potential improvements. Finally, we conclude that neuro-
fuzzy modeling is a promising approach to bridge the gap between rule-based and statistical
NLP for handling messy real-world text.
Methodology
System Architecture Overview
Our proposed software system follows a
pipeline architecture
with tightly integrated neuro-
fuzzy components. Figure 1 illustrates the major components and data flow in the system (from
input text to final output). The architecture is divided into two primary stages: a Fuzzy Text
Preprocessor and a Neuro-Fuzzy Inference Engine.
Fuzzy Text Preprocessor:
This module receives raw text and performs normalization
and feature extraction using fuzzy logic principles. Instead of hard rules that transform
text outright, we use fuzzy matching and gradual scoring. For example, when
encountering an out-of-vocabulary token, the preprocessor computes its similarity to
known words using a fuzzy string matching metric (such as normalized edit distance).
The result is a set of candidate standard words with membership scores indicating how
likely each candidate is the intended word. Misspellings are thus handled by providing a
fuzzy set of possible corrections rather than a single best guess. Similarly, for slang or
abbreviations, the preprocessor may consult a slang dictionary where each entry has a
set of known variants. If a token partially matches a slang term, a fuzzy membership
degree is assigned (e.g., “luv” might have 0.9 membership in the set {“love”} for
sentiment lexicon). In addition to word-level normalization, the preprocessor extracts
features like
noise level indicators
: e.g., the proportion of characters in the text that are
non-alphabetic, or the fraction of words that are OOV (out-of-vocabulary). These are
numeric features that reflect how noisy or irregular the text is. We fuzzify these features
by defining linguistic categories (LOW, MEDIUM, HIGH noise) with appropriate
membership functions. For instance, if 40% of words are OOV, that might give a
“Noise=HIGH” membership of 0.7. Some domain-specific features are also included;
for example, in a social media context, the presence of elongated words (“cooool”) or
excessive punctuation (“!!!”) is a clue to emotional tone – we capture this by a fuzzy
feature like EmphasisDegree.
Neuro-Fuzzy Inference Engine:
The core of the system is a multi-layer neuro-fuzzy
network that takes the fuzzy features (and partially normalized text) from the
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preprocessor and produces the final analysis or decision. We have adopted an
ANFIS-
like architecture
, customized for textual data. Internally, the engine consists of:
1.
Input Layer (Layer 1 – Fuzzification):
Nodes in this layer represent the input
linguistic variables and their fuzzy membership functions. The numeric features
(e.g., noise level, emphasis degree, sentiment lexicon scores) are fed in, and
membership values are computed for each relevant fuzzy set. For example, an
input “percentage of misspelled words = 20%” might yield a membership of 0.4
in LOW-noise and 0.6 in MEDIUM-noise fuzzy sets, and 0 in HIGH-noise.
2.
Rule Layer (Layer 2 – Fuzzy Rule Evaluation):
Each node in this layer
corresponds to a fuzzy rule. We formulate a rule base that combines various
conditions about the text. An example rule could be: “IF NoiseLevel is HIGH
AND ContainsPositiveSlang is TRUE THEN SentimentConfidence is LOW” –
meaning if a text is very noisy but has some positive slang words, we remain
uncertain (low confidence) about positive sentiment. The antecedents of rules
are fuzzy conditions on inputs (which can be truth-valued in [0,1]), and the node
computes the firing strength of each rule by combining the antecedent
memberships (typically using a fuzzy AND = minimum or product operation). In
our implementation we use the product for AND and allow multiple antecedents.
3.
Normalization Layer (Layer 3):
Because multiple rules may fire to different
degrees, this layer normalizes the firing strengths across all rules. Each rule’s
strength is divided by the sum of strengths of all rules, so that they effectively
form a weighted average in influencing the outcome. This ensures stability in the
inference, akin to producing a probability distribution over rule activations.
4.
Defuzzification/Output Layer (Layers 4 & 5):
In a traditional ANFIS for
regression, these layers compute each rule’s output (a linear function of inputs)
weighted by the normalized firing strength, and then sum them up. In our design,
the output of the system can be of two types depending on the application: (a)
a class label or distribution (for classification tasks), or (b) a transformed
text (for a text normalization task). For classification, we use a softmax-like
defuzzification: each class has an output node that aggregates evidence from
rules supporting that class. The final outputs are membership levels for each
class (these can be interpreted as confidence scores for each category). For text
transformation tasks, the output might be a set of suggested normalized tokens.
We implement this by having rules that suggest specific tokens – for instance, a
rule might vote that “ur” should be “your” if certain context conditions hold. The
outputs in that case are a weighted list of token corrections.
What makes this a
neuro-fuzzy
system rather than a pure fuzzy expert system is that many of
the parameters above are learnable from data. The membership function shapes in Layer 1 (e.g.,
the exact thresholds for “HIGH noise”) are tuned during training. The rules in Layer 2 are
initialized based on expert knowledge (or even generated from data patterns), but their
consequent parameters (which determine how strongly a rule indicates a certain output) are also
adjustable. We use a hybrid learning algorithm: gradient descent is applied to the differentiable
parts (membership function parameters), and a least-squares solver is used for linear consequent
parameters, similar to standard ANFIS training. We train the system on a dataset of input texts
with known target outputs (e.g. known true normalized text or known class labels), thereby
optimizing it to handle real-world noise patterns.
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Evaluation Metrics:
For classification, we report standard accuracy, precision, recall,
and F1-score. We pay special attention to performance at different noise levels (0%, 5%, 10%
noise). For normalization, we use word-level accuracy (how many slang/typo tokens are
correctly normalized) and the BLEU score between the system’s output and the reference
cleaned text, as used in the LexNorm challenge. We also conduct a qualitative evaluation of
interpretability: measuring how often the neuro-fuzzy system can provide a correct explanation
(in terms of which rules fired) for its predictions, by manually examining a sample of outputs.
Results
Robustness to Noise in Classification:
The neuro-fuzzy system demonstrated
significantly improved resilience to noisy text compared to the deep learning baseline. Table 1
summarizes sentiment classification accuracy on the IMDB reviews with varying noise rates:
Model
Clean (0% noise) 5% noise 10% noise
BERT fine-tuned
94.5%
90.0%
84.9%
BERT + rule precleaning 94.0%
91.2%
87.0%
Neuro-Fuzzy (ours)
93.8%
92.5%
88.3%
Table 1: Sentiment classification accuracy on IMDB with synthetic noise. (BERT results for
noise levels are from our experiments, aligned with trends reported by Bagla et al. (2021).)
We see that on clean data, all models perform similarly (~94% accuracy). But as noise
increases, vanilla BERT drops to 84.9%, whereas our neuro-fuzzy model still achieves 88.3% at
10% noise – a
4-5 point improvement
, corresponding to roughly 25% fewer errors compared
to BERT. The rule-based precleaning also helped BERT (87.0%), but it underperforms the
neuro-fuzzy approach, likely because our model can learn to correct or accommodate errors that
the fixed rules did not anticipate. These results support our claim that explicitly modeling noise
and ambiguity through fuzzy logic yields a more noise-robust classifier. The SVM baseline (not
shown in the table) started around 88% on clean data and fell to ~80% at 10% noise, indicating
that traditional ML is even more sensitive to noise unless features are carefully normalized.
On the Twitter sentiment dataset (which contained organic noise like hashtags,
abbreviations), we observed a similar pattern. Our method achieved an F1-score of 0.82,
compared to 0.74 for BERT when both were trained on noisy tweets. Particularly, in cases with
heavy slang, the neuro-fuzzy model often correctly gauged sentiment where BERT
misclassified. For example, a tweet: "happy bday bro.. u r da best!!" was classified as positive
by our system (fuzzy matching “bday”→ birthday, “da”→the, and recognizing “happy” and
“best” as positive signals), whereas BERT tuned on standard data struggled with “bday” and
“da” being OOV and predicted non-positive.
Spam Detection and Intent Classification:
In the SMS spam dataset, our approach also
excelled at catching spam messages that use obfuscation. Many spam texts deliberately insert
special
characters
or
funky
spelling
to
evade
filters
(e.g.,
“W1n now!!!”).Theneuro−fuzzysystem’srulebaseincludedpatternsforsuchobfuscation(likeafuzz
yregexforwordslike“win”withnumbersreplacingletters).Itachieved99.0now!!!”).Theneuro−fuzzy
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system’srulebaseincludedpatternsforsuchobfuscation(likeafuzzyregexforwordslike“win”withnu
mbersreplacingletters).Itachieved99.0” or “credit card”) could be cross-checked, and if context
was lacking, the system held back from a full “spam” classification (outputting moderate
membership to spam). This showcases the benefit of having a graded output – the system can
express uncertainty. A message like “Call now to claim your prize 100% free” gets a spam
score of 0.9 (very likely spam) due to multiple fuzzy cues, whereas “This is 100% legit, call
me” might only score 0.4 as spam because, though it has “100%”, other spam indicators are
absent.
In the intent classification of short queries (like Example 2 earlier), we evaluated on a
small custom dataset of chatbot queries (1000 examples across 5 intent categories). Our model
achieved an overall accuracy of 88%, outperforming a purely neural classifier at 81%. Notably,
for the most ambiguous queries (single-word or two-word inputs), the neuro-fuzzy system was
correct 75% of the time vs 60% for the neural model. This is a significant improvement in a
high-ambiguity regime. The fuzzy rules helped by leveraging context like whether the word
was an imperative verb or a noun, etc., whereas the neural model often had to guess from an
embedding alone.
Discussion
The above results and examples demonstrate the viability of neuro-fuzzy modeling for
problematic text data. In this section, we discuss key observations, limitations of the current
system, and potential improvements.
Robustness and Generalization:
One of the most positive findings is the robustness of the
system to diverse noise. By explicitly modeling uncertainties (e.g., whether “guuud” means
“good” with 0.8 confidence), the system doesn’t commit to a wrong interpretation too early.
Instead, multiple hypotheses can be carried through via fuzzy memberships. This proved
beneficial when noise was present in forms not seen in training – the fuzzy logic provided a
form of built-in generalization by handling “similar” inputs similarly. For example, even if
“loooove” (with 4 ‘o’s) was not seen in training, the rule for elongated “love” with any number
of ‘o’s still catches it. In contrast, a pure neural model might not connect “love” and “loooove”
strongly unless it saw many examples. This suggests that incorporating domain knowledge
through fuzzy rules can make NLP models more data-efficient on noisy domains. Our system
needed smaller training data for similar performance, because the fuzzy rules already
encapsulated some patterns (a form of inductive bias).
Interpretability vs. Accuracy Trade-off:
While our approach achieved “reasonable
accuracy with high interpretability” as intended, there is often a trade-off between these aspects.
Pure deep models could potentially surpass our model’s accuracy if given very large training
corpora (especially on clean text). We consciously chose to accept a slight accuracy hit on clean
data in exchange for robustness and interpretability. For instance, our best clean accuracy on
IMDB (93.8%) was marginally below BERT’s (94.5%). But on noisy data, we overtook BERT
by a good margin. In applications where noise is expected, this trade-off is worthwhile.
However, if an application domain always has perfectly edited text (e.g., formal news articles),
a simpler deep model might suffice. A potential future direction is to combine the power of
transformers with fuzzy logic – e.g., use BERT embeddings as inputs to a fuzzy layer – to see if
we can get the best of both: high raw accuracy and noise handling.
Limitations:
Despite its strengths, our system has limitations. One limitation is
the
reliance on a predefined fuzzy rule base and features
. We observed that performance and
coverage are only as good as the rules/features we supply. For languages or domains with very
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different slang or noise patterns, an expert would need to craft new rules or lexicons. This
knowledge engineering aspect could be time-consuming. The system does learn and adjust
parameters, but if a phenomenon is not represented in any initial rule or feature, the network
alone may not invent it. For example, if a particular type of obfuscation (say, alternating
uppercase/lowercase to evade filters) was not in our feature set, the current system would likely
miss it. This points to the need for
automatic rule induction
. One idea is to mine rules from
data by looking at what differentiates misclassified examples – akin to how decision trees split
features, we could generate fuzzy rules on the fly. Some research on neuro-symbolic learning
could be applied here so that the model can propose new linguistic patterns to add to its
repertoire.
Another limitation is
scalability
. Fuzzy inference as implemented (especially if we had
very large rule sets) can be computationally heavier than a single matrix multiplication in a
neural net. Our prototype with ~20 rules is extremely fast, but if one attempted to have
hundreds of rules or very fine-grained membership sets for thousands of words, the inference
might slow down. There is ongoing research on optimizing fuzzy computations and even
differentiable fuzzy logic that could help scale to more rules or integrate into GPU-friendly
frameworks.
We also note that our evaluation focused on relatively short texts (tweets, SMS, single
sentences or reviews). For longer documents or more complex NLP tasks (like parsing or
summarization), the method would need extension. It’s conceptually possible to apply neuro-
fuzzy modeling to any NLP task, but performance on tasks requiring deep understanding (e.g.,
QA, logical inference) might require more sophisticated rule design (perhaps higher-order fuzzy
logic or fuzzy knowledge graphs). Encouragingly, some recent works on fuzzy-NLI and fuzzy
common-sense reasoning indicate it’s feasible to incorporate logical rules for complex
reasoning Our work primarily tackled surface-level noise and classification ambiguity; handling
higher-level ambiguity (like resolving pronoun references fuzzily) is an open challenge.
Comparison with Other Approaches:
It is insightful to compare our neuro-fuzzy approach
with alternative strategies for noisy text. One alternative is adversarial training – training neural
nets on augmented data with noise so they learn to be invariant. While effective to some extent,
adversarial training tends to make the model robust to the specific noise seen; it might not
generalize to qualitatively different noise. The neuro-fuzzy approach, by contrast, inherently
covers a broad range of noise through linguistic generalizations. Another approach is using edit
distance or character-level models directly in neural nets (like character CNNs or RNNs) to
cope with spelling variation. These models do help with noise, but they sacrifice interpretability
and can sometimes misfire (e.g., treating two different words as same if spelled similarly). Our
approach explicitly knows what is a spelling error versus a word choice because of separate
features.
Potential Improvements:
Several avenues can enhance the system:
Type-2 Fuzzy Logic:
In some cases, even the membership functions have uncertainty
(especially when dealing with human annotations or vague categories). An extension
would be to use interval type-2 fuzzy sets for some features, which can model
uncertainty in the degree of membership itself. This could make the system even more
robust to noise in the input features (e.g., if we are uncertain whether something is slang
or just a typo, we can represent that uncertainty).
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Dynamic Rule Refinement:
Using feedback from errors to refine rules. For instance, if
the system consistently mis-normalizes a certain new slang, we can add a rule for it.
This could be done in an online learning fashion.
Integration with Large Language Models:
We can envision a hybrid where a large
language model (LLM) like GPT-3 is used to suggest possible interpretations of an
ambiguous text, and then our fuzzy system uses those suggestions as inputs or as
additional rules. The fuzzy system could act as a governor that checks the LLM’s
suggestions against logical rules (in line with efforts to make LLMs more controllable).
Multilingual Extension:
Fuzzy logic is language-agnostic, but the specific rules and
lexicons are not. Extending the system to multilingual settings would require multi-
language slang dictionaries and possibly language-specific features (e.g., some
languages have more compounding, so splitting words might need fuzzy handling).
Given the modular design, one could plug in language-specific fuzzy knowledge bases
to handle, say, Arabic chats or Chinese pinyin slang.
In summary, the discussion highlights that our neuro-fuzzy system strikes a practical balance
between performance and interpretability for noisy text. It is robust and gives insight into its
decisions, but it requires careful design of fuzzy knowledge. The limitations point to interesting
research directions bridging automatic learning of rules and integration with modern deep NLP.
As NLP applications continue to grapple with “messy” language input, we believe approaches
like this will become increasingly relevant, perhaps in tandem with purely neural solutions, to
ensure both
accuracy and reliability
in AI language understanding.
Conclusion
In conclusion, neuro-fuzzy modeling offers a promising pathway to handle
the messiness of real textual data. It aligns well with the current drive in AI for models that are
not only accurate but also
explainable
and
trustworthy
. While purely neural models have
achieved remarkable successes on clean, benchmark data, the real world demands systems that
can gracefully handle the unexpected – and do so in a way that humans can understand. The
work reported in this paper takes a significant step in that direction for the domain of text
processing. Future work will aim to further automate the incorporation of knowledge into
neuro-fuzzy models and to integrate these ideas with the latest advances in language modeling,
ultimately striving for NLP systems that can think a bit more like humans – dealing with
uncertainty and ambiguity using commonsense rules, yet learning and refining their
understanding from experience.
References:
1. Bagla, K., Natarajan, T., & Swaminathan, M. (2021). Impact of textual noise on NLP
models. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14951–
14959.
2. Bodyanskiy, Y., & Vynokurov, D. (2012). Modified probabilistic neuro-fuzzy network for
text document classification. Automatic Control and Computer Sciences, 46(8), 346–353.
3. Fitri, M. H., Ishak, I., & Mahayuddin, N. A. (2024). Text normalization approach for noisy
Malay-English social media texts. Malaysian Journal of Computer Science, 37(1), 12–22.
4. Han, B., Cook, P., & Baldwin, T. (2012). Automatically constructing a normalisation
dictionary for microblogs. Proceedings of the 2012 Joint Conference on Empirical Methods
in Natural Language Processing and Computational Natural Language Learning, 421–432.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 07,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 581
5. Jang, J.-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE
Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.
6. Kumar, S., Dandapat, S., & Bhattacharyya, P. (2020). Robustness of deep learning models
for NLP: A survey. arXiv preprint arXiv:2007.07393.
7. Liu, Q., Xie, X., Zhou, M., & Wang, Y. (2024). Fuzzy logic and natural language
processing: A survey of hybrid techniques. Journal of Artificial Intelligence Research, 71,
113–145.
8. Rustamov, S. R. (2013). Neuro-fuzzy modeling of text sentiment analysis. Scientific Journal
of Informatics, 14(1), 35–43.
9. Specht, D. F. (1990). Probabilistic neural networks. Neural Networks, 3(1), 109–118.
10. Subramaniam, L. V., et al. (2009). Challenges in processing noisy text. Proceedings of the
ACL-IJCNLP 2009 Conference Short Papers, 127–130.
11. Taghva, K., Borsack, J., & Condit, A. (2000). Effects of OCR errors on document image
retrieval. Information Processing & Management, 36(4), 725–740.
12. Vashishtha, S., & Susan, S. (2021). MultiLexANFIS: A neuro-fuzzy approach to social
media sentiment analysis. Expert Systems with Applications, 183, 115357.
13. Wu, Y., et al. (2016). Cleaning as a service: A framework for online text normalization.
Proceedings of the 25th International Conference on World Wide Web, 271–282.
14. Wu, S. (2022). Neural fuzzy logic reasoning for natural language inference. Proceedings of
the 60th Annual Meeting of the Association for Computational Linguistics, 5234–5245.
