NLP IMP

 Word Sense Systems :

1. The first generation of word sense disambiguation systems relied on dictionary sense definitions and handcrafted techniques.

2. Many of these techniques and resources used in the early systems are not readily accessible today.

3. The Lesk algorithm, a dictionary-based approach, determines the sense of a word based on the overlap between its dictionary definition and the context.

4. The Lesk algorithm selects the sense with the highest overlap as the best sense for the word.

5. Another dictionary-based algorithm suggested by Yarowsky used Roget's Thesaurus categories for disambiguation.

6. Yarowsky's method classified unseen words into Roget's Thesaurus categories based on statistical analysis.

7. The success of Yarowsky's method was demonstrated on a set of 12 words with previous quantitative studies.

8. Yarowsky's algorithm involves three steps: collecting contexts, computing weights for salient words, and classifying unseen words into categories.

9. Rule-based systems provide a relatively simple and interpretable approach to word sense disambiguation.

10. Rule-based systems rely on predefined rules or patterns and may combine linguistic or lexical resources for disambiguation.

1. The supervised approach to word sense disambiguation is considered superior to unsupervised methods and performs best when tested on annotated data.

2. Supervised systems rely on machine learning classifiers trained on manually disambiguated words in a corpus.

3. The downside of the supervised approach is that the sense inventory must be predetermined, and any changes require expensive reannotation.

4. Support vector machines (SVMs) and maximum entropy (MaxEnt) classifiers are commonly used and high-performing classifiers for word sense disambiguation.

5. Separate models are typically trained for each lemma and part of speech (POS) combination.

6. Lexical context, parts of speech, bag of words context, local collocations, syntactic relations, and topic features are commonly used features in supervised word sense disambiguation.

7. Lexical context features include the words and lemmas occurring in the paragraph or a smaller window around the target word.

8. Parts of speech features provide information about the POS of words surrounding the target word.

9. Bag of words context features use an unordered set of words in the context window, including the most informative ones.

10. Local collocations are ordered sequences of phrases near the target word that provide semantic context.

11. Syntactic relations can be used if the sentence parse is available, providing information about the relationships between words.

12. Topic features indicate the broad topic or domain of the article that the word belongs to.

13. Additional rich features for disambiguation include voice of the sentence, presence of subject/object, sentential complement, prepositional phrase adjunct, named entity, and WordNet synsets.

14. The voice of the sentence feature indicates whether the sentence is passive, semipassive, or active.

15. Feature selection is often performed per-word to determine the best set of features for a particular word.

1. The progress in word sense disambiguation is hindered by the scarcity of labeled training data for training a classifier for each sense of every word in a given language.

2. To address this problem, there are several solutions that can be employed.

3. One approach is sense induction through clustering, where instances of a word are clustered together to constrain examples to specific senses. This helps in effectively grouping similar instances.

4. Another solution involves using distance metrics to determine the proximity of a given instance to sets of known senses of a word. The closest sense is then selected as the most appropriate sense for that instance.

5. The process of clustering and sense induction can be iterative, starting with seed examples of certain senses and gradually expanding and refining the clusters.

6. However, the discussion does not delve into clustering-based sense induction methods in detail, assuming the presence of a predefined sense inventory.

7. Unsupervised methods discussed here rely on very few, if any, hand-annotated examples. They aim to classify unseen test instances into predetermined sense categories.

8. One category of algorithms utilizes distance measures to identify senses. Rada et al. introduced a metric that computes the shortest distance between pairs of senses in WordNet.

9. Resnik proposed a measure of semantic similarity called information content, which performs better than the traditional edge-counting measure. It considers the hierarchical structure of an IS-A taxonomy.

10. Agirre and Rigau further enhanced the measure by introducing conceptual density. This measure not only considers the number of separating edges but also accounts for the depth of the hierarchy and the density of its concepts.

11. Conceptual density is defined for each subhierarchy within the sense inventory. The sense that falls within the subhierarchy with the highest conceptual density is chosen as the correct sense.

These points encompass all the details mentioned in the provided context regarding the dearth of labeled training data and the solutions proposed, including clustering, distance metrics, and the refinement of measures for sense identification.

Certainly! Here are five main points summarizing the information about the Yarowsky algorithm and its assumptions:

1. The Yarowsky algorithm is a classic semisupervised method for word sense disambiguation that starts from a small seed of labeled examples and iteratively expands the training data using a classifier.

2. The algorithm is based on two key assumptions about corpora:

   a. One sense per collocation: The syntactic relationship and the surrounding words near a word provide strong indications of its sense.

   b. One sense per discourse: Instances of the same lemma within a given discourse typically invoke the same sense.

3. Leveraging these assumptions, the Yarowsky algorithm iteratively disambiguates most of the words in a discourse, utilizing the labeled examples to train the classifier and generate better predictions for subsequent selection cycles.

4. The algorithm relies on the observation that the contextual information and collocations associated with a word can provide valuable clues for its sense disambiguation.

5. By iteratively expanding the training data with automatically labeled examples, the Yarowsky algorithm aims to improve the accuracy of word sense predictions and tackle the problem of limited labeled training data.

These points capture the main aspects of the Yarowsky algorithm and its underlying assumptions, emphasizing its iterative nature and the utilization of contextual information for word sense disambiguation.

The late 1990s marked the emergence of two significant corpora that are semantically tagged: FrameNet and PropBank. These resources have shifted the focus from rule-based approaches to more data-oriented approaches in natural language processing.

1. FrameNet:

   - FrameNet is based on the theory of frame semantics, where a predicate invokes a semantic frame that represents a conceptual structure.

   - FrameNet contains frame-specific semantic annotation of predicates in English, extracted from the British National Corpus (BNC).

   - The annotation process involves identifying specific semantic frames and creating frame-specific roles called frame elements.

   - Predicates that instantiate the semantic frame are identified, and sentences are labeled for those predicates, mapping the frame elements.

   - Each sense of a polysemous word is associated with a unique frame, and the pairing of a word with its meaning is called a lexical unit (LU).

   - FrameNet captures both literal and metaphorical interpretations of text, and it has been extended to multiple languages.

2. PropBank:

   - PropBank is based on Dowty's prototype theory and utilizes a linguistically neutral view of predicate-argument structure.

   - PropBank focuses on verb predicates and provides annotations of arguments for different verb senses.

   - Core arguments (ARGN) are specific to each verb sense, while adjunctive arguments (ARGM-X) have consistent meanings across predicates.

   - Arguments are labeled based on their roles in the sentence, such as PROTO-AGENT (ARG0) and PROTO-PATIENT (ARG1).

   - PropBank builds on the syntactic Penn Treebank corpus and has been expanded to include annotations from various genres through the OntoNotes project.

Other Related Resources:

- NomBank: Inspired by PropBank, NomBank identifies and tags arguments of nouns, expanding the NOMLEX dictionary.

- VerbNet: VerbNet associates PropBank frames with predicate-independent thematic roles and provides richer representations by linking framesets with Levin verb classes.

- Prague Dependency Treebank: This resource tags the predicate-argument structure in its tectogrammatic layer on top of the dependency structure, with distinctions between inner participants and free modifications.

- NAIST Text Corpus: Influenced by Japanese linguistic traditions, this corpus incorporates predicate-argument structure annotations.

FrameNet and PropBank have served as models for similar projects in multiple languages, expanding the availability of semantically tagged resources worldwide and facilitating research in predicate-argument recognition and semantic analysis.

In the context of semantic role labeling (SRL), several systems have been developed with various features to tackle the task. These systems aim to identify and classify the semantic arguments of predicates in sentences. Here is an overview of different systems and the features they employ:

1. Gildea and Jurafsky System:

   - FrameNet and PropBank were influential in the development of this system.

   - Semantic role labeling is treated as a supervised classification problem.

   - Three tasks are introduced: argument identification, argument classification, and their combination.

   - Features used include path, predicate lemma, phrase type, position, voice, head word, subcategorization, and verb clustering.

2. Surdeanu et al. System:

   - Additional features are suggested to improve SRL performance.

   - Content word heuristics are used to identify informative constituents.

   - Part of speech of the head word and content word are added as features.

   - Named entity information is incorporated as binary-valued features.

   - Phrasal verb collocations and other statistical features are employed.

3. Fleischman, Kwon, and Hovy System:

   - Features added to this system include logical function, order of frame elements, syntactic pattern, and previous role.

   - Logical function represents the role type of an argument.

   - Syntactic pattern is determined based on phrase type and logical function.

   - Previous role features indicate the previous observed/assigned role for the current predicate.

4. Pradhan et al. System:

   - This system suggests various feature variations to enhance SRL performance.

   - Named entities in constituents are used as features.

   - Verb sense information is employed to capture the sense-dependent argument sets.

   - Noun head of prepositional phrases is considered to improve discrimination.

   - First and last word/POS in a constituent, ordinal constituent position, constituent tree distance, constituent relative features, temporal cue words, and dynamic class context are among the additional features used.

   - Path generalizations, such as clause-based variations, path n-grams, single-character phrase tags, path compression, directionless path, and partial path, are utilized to overcome sparsity and improve generalization.

5.The Gildea and Hockenmaier System is a semantic role labeling system that introduced three features to improve the performance of the task:

1. Phrase type: This feature captures the category of the maximal projection between the predicate and the dependent word. It helps in determining the syntactic relationship between the two words. For example, the phrase type for the words "denied" and "plans" in the sentence would indicate the grammatical category of the phrase connecting them.

2. Categorial path: This feature is formed by concatenating three values: the category to which the dependent word belongs, the direction of dependence (whether it is a forward or backward dependency), and the slot in the category filled by the dependent word. It provides information about the syntactic structure and dependency relations between the predicate and the dependent word. For example, the categorial path between "denied" and "plans" in the given tree would be represented as "(S[dcl]\NP)/NP.2.←", indicating the syntactic path from the dependent word to the predicate.

3. Tree Path: This feature is similar to the path feature in the Charniak parse-based system. It traces the path from the dependent word to the predicate through the binary CCG (Combinatory Categorial Grammar) tree. It captures the hierarchical syntactic structure and the relationship between the predicate and the dependent word based on the CCG tree. The tree path provides a categorial representation of the path, representing the syntactic composition and combination of categories along the path.

These features introduced by Gildea and Hockenmaier enhance the semantic role labeling system by incorporating syntactic information and capturing the structural dependencies between the predicate and its arguments.

Several resources and projects have contributed to the development and experimentation of natural language understanding and knowledge representation. Here are some notable resources:

1. ATIS (Air Travel Information System): ATIS was an early project focused on transforming natural language queries about flight information into a representation usable by an application. It employed a hierarchical frame representation to encode intermediate semantic information. The system would convert user queries into SQL queries to extract answers from a flight database. The training corpus included transcribed utterances, categorized and annotated with reference answers, and a subset of them were treebanked.

2. Communicator: Communicator was the successor to ATIS and involved mixed-initiative dialogue. It facilitated real-time travel information and itinerary negotiation between humans and machines. The Linguistic Data Consortium made available thousands of annotated dialogs from the program. Carnegie Mellon University also collected additional data, including annotated dialogs with dialog acts.

3. GeoQuery: GeoQuery is a natural language interface (NLI) to a geographic database called Geobase. It allows users to query information about U.S. geography using natural language. The database contains facts stored in a relational database, such as population, neighboring states, major rivers, and cities. The GeoQuery corpus has been translated into multiple languages, including Japanese, Spanish, and Turkish.

4. Robocup: Robocup is an international initiative that employs robotic soccer as its domain for artificial intelligence research. Within Robocup, there is a formal language called CLang used to encode advice from team coaches, and behaviors are expressed as if-then rules. This language enables communication and coordination among autonomous agents in the soccer game.

These resources have provided valuable datasets, representations, and frameworks for natural language understanding and knowledge representation research, fostering advancements in the field.

Certainly! Here are some examples to illustrate the resources mentioned:

1. ATIS (Air Travel Information System):

   - User Query: "What flights are available from New York to Los Angeles tomorrow?"

   - Frame Representation: [FlightInfo: {DepartureCity: "New York", DestinationCity: "Los Angeles", Date: "Tomorrow"}]

2. Communicator:

   - Dialog:

     - User: "I need to fly from Boston to Chicago on Friday."

     - Machine: "Sure, I found several options for you. Here are the flight details..."

   - Dialog Act Annotation: [User: Inform(DepartureCity: "Boston", DestinationCity: "Chicago", Date: "Friday")]

   

3. GeoQuery:

   - Query: "What is the capital of the state with the largest population?"

   - Representation: answer(C, (capital(S, C), largest(P, (state(S), population(S, P)))))

4. Robocup: CLang:

   - Rule: If the ball is in our penalty area, all our players except player 4 should stay in our half.

   - CLang Representation: ((bpos (penalty-area our)) (do (player-except our 4) (pos (half our))))

These examples demonstrate how each resource handles natural language queries or dialogues and converts them into representations suitable for further processing or interaction with databases, systems, or robotic agents.

Rule-based systems and supervised systems are two approaches used in semantic parsing to map natural language to meaning representations. Here are 10 key points about each approach:

Rule-Based Systems:

1. Rule-based systems in semantic parsing utilize an interpreter with a handcrafted semantic grammar designed to handle speech recognition errors.

2. The focus is on parsing the meaning units in a sentence and extracting the underlying semantic information, considering that it is less complex than the syntactic structure.

3. Rule-based systems are robust in dealing with ungrammatical instructions, stutters, filled pauses, and other aspects of spontaneous speech.

4. Word order becomes less important in rule-based systems, as meaning units can be scattered in sentences and may not follow syntactic order.

5. Phoenix, a system developed by Ward, employs recursive transition networks (RTNs) and a handcrafted grammar to extract a hierarchical frame structure. It adjusts the values of these frames with each new piece of information.

6. Phoenix achieved an error rate of 13.2% for spontaneous speech input and 9.3% for transcript input in the ATIS and Communicator projects.

7. Rule-based systems require substantial upfront effort to create the rules and are often limited to specific domains due to the time and specificity required.

8. Maintenance and scalability of rule-based systems become challenging as the complexity and domain independence of the problem increase.

9. Rule-based systems tend to be brittle, meaning they are sensitive to changes or variations in the input and may not generalize well.

10. Rule-based systems have paved the way for subsequent research and improvement in semantic parsing techniques.

Supervised Systems:

1. Supervised systems in semantic parsing use statistical models derived from hand-annotated data to map natural language to meaning representations.

2. Statistical models are trained using annotated data, which helps in dealing with unknown phenomena and improving system performance.

3. Schwartz et al. developed one of the first end-to-end supervised statistical learning systems for the ATIS domain, comprising semantic parse, semantic frame, discourse, and backend components.

4. The supervised learning approach is combined with quick training augmentation through human-in-the-loop corrective approaches to enhance supervision.

5. Miller et al. achieved an error rate of 14.5% on the entire test set and 9.5% on context-independent sentences using their supervised system.

6. He and Young, among other researchers, have made further improvements on supervised systems since then.

7. Natural language interface for databases (NLIDB) is a commonly known application area where supervised systems are used.

8. NLIDB systems, such as Zelle and Mooney's CHILL, convert natural language questions into database queries, leveraging relational learning techniques.

9. Supervised systems benefit from machine learning and syntactic parsing advancements to improve accuracy and performance.

10. Techniques like SCISSOR, KRISP, and WASP have been developed, combining statistical syntactic parsing, string kernels, machine translation, and alignment approaches to enhance supervised semantic parsing systems.

Certainly! Here are five applications and limitations of the N-gram model as described in the given context:

Applications:

1. Speech Recognition: The N-gram language model is used in speech recognition to correct errors caused by noise in the input. By leveraging probability knowledge, the model helps improve the accuracy of converting speech to text.

2. Machine Translation: In machine translation, the N-gram model is employed to generate more natural and fluent statements in the target language. It aids in producing translations that are contextually appropriate and linguistically accurate.

3. Spelling Error Correction: The N-gram language model is useful for correcting spelling errors. It can identify errors such as the incorrect usage of valid words in a given context, such as distinguishing between "minuets" and "minutes" in the phrase "in about fifteen minutes."

4. Language Classification and Differentiation: By analyzing the N-gram model, languages can be classified or differentiated, such as distinguishing between US and UK spellings. The model can capture language-specific patterns and variations.

5. Various NLP Applications: The N-gram model benefits several NLP applications, including part-of-speech tagging, natural language generation, word similarity calculation, and sentiment extraction. It provides valuable insights into language patterns and helps improve the performance of these tasks.

Limitations:

1. Out-of-Vocabulary Words: The N-gram model faces challenges when encountering words during testing that were not present in the training data. Handling out-of-vocabulary words requires techniques such as using fixed vocabularies and converting unknown words to pseudowords.

2. Scalability to Larger Datasets: Scaling the N-gram model to larger datasets or moving to higher orders (e.g., higher n-grams) requires better feature selection approaches. The increase in the number of features can impact computational efficiency and memory requirements.

3. Poor Handling of Long-Distance Context: The N-gram model has limitations in capturing long-distance dependencies or context in language. It may struggle to consider words or phrases that are farther apart in a sentence, resulting in less accurate predictions for such cases.

4. Performance Gain Plateau: After a certain point, usually around 6-grams, the performance gain of the N-gram model becomes limited. The predictive power of longer n-grams diminishes, and the model may not significantly improve with further increasing the order.

5. Contextual Ambiguity: The N-gram model relies on local context and may encounter difficulties in disambiguating words or phrases with multiple possible interpretations. Resolving such ambiguities often requires considering a broader context or employing more sophisticated language models.

These applications and limitations provide insights into the practical usage and challenges associated with the N-gram model in NLP tasks.