Identifying Intent-Sentiment Co-reference from Legal Utterances (2023)

Abstract: Co-reference is always treated as one of challenging tasks under natural language processing and has been explored only in the domain of anaphora resolution to an extent. However, the benefit of it to identify the relations between multiple entities in a single context can be explored better while we aim to identify intent and sentiment from the utterances of a dialogue or conversation. The utilization of co-reference becomes more elegant while tracking users’ intents with respect to their corresponding sentiments explored in a specialized domain like judiciary. Thus, in the present attempt, we have identified not only intent and sentiment expressions at token level in an individual manner, we also classified the utterances and identified the co-reference between intent and sentiment entities in utterance level context. Last but not the least, the deep learning algorithms have shown improvements over traditional machine learning in all cases.

## introduction discourse, a one-way dialogue where both parties participate and one of the main purposes of a cooperative, two-way conversation is to transfer knowledge from the speaker/writer to the listeners/readers. discourse study 1 in general aims to answer two types of questions: (1) what information is provided in the extended sequences of utterances that are not contained in the meaning of the individual utterances? (2) what effect does the context in which an utterance is used have on the meaning of individual utterances or parts of them? one of the reasons for considering discourse and dialogue rather than just the sentences is because information is sometimes presented or requested over multiple sentences, and we want to recognise various phrases or relationships among them that identify the who, what, when, where, and why of the event. we could take material from articles in newspapers and magazines, as well as chapters from books, and save it in tables that are more easily searchable for dialogue. we might want to extract information from dialogue to complete activities like booking travel plan or making a restaurant reservation, or teaching a kid etc. alternatively, we might wish to be able to detect explicit or implicit demands in purely social exchanges that have no clear purpose. intent or sentiment identification in an individual manner may not always contribute whereas their relationship in terms of co-reference actually helps in tracking these entities during the discourse trail, from start to the end. intents are the tags that we can assign to words or phrases in a dialogue or discourse dataset. it differs from topic identification in general as topic identification itself is a separate research area @xcite . however, it identifies what fine-grained topics/aspects are being discussed in utterances or in dialogues. intents are subjective to the conversation topic; there can be unique sets of intents for different conversations. let's take two sentences from a chat as follows, 1. i am hungry. ## related work intent identification: the authors (xu and sarikaya, 2013) employed cnn followed by triangular crf to forecast intent and identify an entity using extracted characteristics from cnn. on the other hand, @xcite proposed elman-type, jordan-type, and bi-directional jordan-type rnn followed by basic crf and later peng et al. (2014) employed a modified deep lstm, followed by crf to explicitly represent the dependencies between semantic labels for greater understanding. the intent identification was further explored by using bi-directional rnn, bidirectional rnn with attention mechanism, and encoder-decoder lstm by several researchers @xcite . in @xcite , a self-attentive shared encoder has been used to produce better contextaware representations which applied as the extracted and summarized features for ic at sentence and the token level. recently in @xcite , authors have used a pre-trained bert model and a fine-tuned bert model for ic and joint ic -slot filling tasks, respectively. ## intent extraction it has been observed that the identification of intent words is more crucial than classifying intents. moreover, the task to identify the words or tokens that are responsible for that particular utterance related to that intent has been performed first. there can be one or more than one word that can be marked as intent-word. moreover, the utterances can contain one or more than one word that can be potentially intent word so that we later on can deduct the relationship between intent and sentiment by using these words. the main motivation of our task is to identify the potential feature words that better represent the intent classes. therefore, we first use tf-idf technique to extract feature words that potentially can be used for identifying the intent-word. we prepared a dataset and marked the feature words that are present in the utterances as potentialintent words. secondly, we prepared a dataset to mark tokens in the utterances if they are intent words but we did not feed them into an entity recognition model. the utterances are broken into tokens; so each row of the dataset contains the tokens, their corresponding sentence number, pos tag and entity recognition tag. ## sentiment extraction the second challenge is to determine which words or tokens are responsible for that specific sentiment in an utterance. there can be one or more words that are identified as sentimentwords. our goal is to find words that better convey different sentiment classes so that we can later use these words to deduce a correlation between intent and sentiment. we must first identify the feature words that determine the sentiment classes. in order to achieve this task, we first utilise text-blob 4 to extract feature words that could be used to identify sentiment of the whole utterance. we prepared a dataset and label feature words that appear in utterances as potential sentimentwords. second, we developed a dataset to label tokens in utterances as sentiment-words or not, which we used to feed into a bert-based entity identification model to discover potential sentiment classes in the dialogue. tokens have been employed from the utterances, and each row of the dataset contains the tokens, their related sentence number, pos and entity recognition tag. ## intent-sentiment co-reference one of the primary objectives of co-reference resolution is to locate all phrases in a text that refer to the same thing. it's a crucial stage in a variety of higher-level nlp activities involving natural language comprehension, such document summarising, question answering, and information extraction. although co-reference resolution has been used in noun-noun or nounpronoun based resolution, we in this present work are trying to detect co-reference between the intent of user in a dialogue or conversation to the sentiment of the user. the main purpose of detecting intent-sentiment co-reference is to better understand how sentiment of a user depends on the topics they are discussing. better scoring means those topics are more sensitive to the users, hence the conversation needs better monitoring. this significantly improves the performance of a chat-bot to mimic human interactions. we divided the detection of co-reference into two sections. first, we will discuss how to detect if utterances have any sentiment-word co-relates to intent-word. we are going to achieve that we prepared lexicons containing intent and sentiment words and we parse the word dependency using stanford nlp parser (stanza) to detect if any relation between them exists. pre-processing -we have used the dependency parser developed by stanford namely "stanza". the dependency parser takes an utterance as input and generates all the dependencies possible between each and every token. from the input phrase, the dependency parsing module creates a tree structure of words that depicts the relationships between words' syntactic dependencies. ## conclusion the goal of co-reference resolution is to find all phrases that refer to the same entity in a text. it's an important step in a number of higher-level nlp tasks requiring natural language understanding, such as document summarization, question answering, and information extraction. despite the fact that co-reference resolution has been employed in noun-noun or noun-pronoun based resolution, we are attempting to identify co-reference between the user's purpose in a dialogue or discussion and the user's sentiment in this thesis study. the basic goal of intentsentiment co-reference detection is to better comprehend how a user's sentiment varies depending on the subjects they're talking. better ranking indicates that certain issues are more sensitive to users, necessitating more careful monitoring of the discourse. this boosts the ability of chat-bot ai to simulate human interactions dramatically. the detection of co-reference was split into two sections. to begin, we identified all of the utterances in which a sentiment-word is related to an intent-word. we were able to accomplish this by using a database that already contained intent-word and sentiment word information. we then used the stanford nlp parser (stanza) to analyze the word dependence and discover coreference between sentiment and intent.